Digital Platform for Trading and Management of Investment Securities

ABSTRACT

A stratified or segmented composite data structure can be formed by selecting a group of data entities, stratifying or segmenting them according to attributes, and assigning relative weights to the components based on their stratified or segmented positions. The attributes are selected from a universe of possible values. Further positive and negative biases can be applied at any arbitrary point or position, including to individual data entities, groups of arbitrarily selected data entities, or arbitrary positions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 16/542,252,filed Aug. 15, 2019, now U.S. Pat. No. 11,227,028, which is acontinuation-in-part of application Ser. No. 16/237,735, filed Jan. 1,2019, now U.S. Pat. No. 10,515,123, which is a continuation ofapplication Ser. No. 16/006,601, filed Jun. 12, 2018, now U.S. Pat. No.10,191,888, which is a continuation of application Ser. No. 15/589,922,filed May 8, 2017, now U.S. Pat. No. 9,996,502, which is acontinuation-in-part of application Ser. No. 15/006,108, filed Jan. 25,2016, now U.S. Pat. No. 9,646,075, which is a continuation ofapplication Ser. No. 14/801,775, filed Jul. 16, 2015, now U.S. Pat. No.9,245,299, which is a continuation-in-part of application Ser. No.14/604,197, filed Jan. 23, 2015, now U.S. Pat. No. 9,098,878, whichclaims the benefit of U.S. Provisional Application No. 61/930,807, filedJan. 23, 2014, and which is a continuation-in-part of application Ser.No. 14/216,936, filed Mar. 17, 2014, now U.S. Pat. No. 8,990,268, andwhich claims the benefit of U.S. Provisional Application Ser. No.61/801,959, filed Mar. 15, 2013, and U.S. Provisional Application Ser.No. 61/802,245, filed Mar. 15, 2013, the contents of all of which areherein incorporated by reference in their entirety. application Ser. No.14/801,775, filed Jul. 16, 2015, now U.S. Pat. No. 9,245,299, is also acontinuation-in-part of application Ser. No. 14/604,272, filed Jan. 23,2015, now U.S. Pat. No. 9,098,564, which is a divisional of applicationSer. No. 14/216,936, filed Mar. 17, 2014, now U.S. Pat. No. 8,990,268,which claims the benefit of U.S. Provisional Application Ser. No.61/801,959, filed Mar. 15, 2013, and U.S. Provisional Application Ser.No. 61/802,245, filed Mar. 15, 2013, the contents of all of which areherein incorporated by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates generally to computerized techniques foralgorithmically determining the composition of elements in a functionalsystem represented in n-dimensional space.

BACKGROUND OF THE INVENTION

The management of investment portfolios has been the subject ofsubstantial theory and research. Portfolio theory considers how wealthshould be invested and how to maximize a portfolio's expected return fora given amount of portfolio liquidity-adjusted risk, or, equivalently,minimize liquidity-adjusted risk for a given level of expected return,by carefully choosing the proportions of various assets. While a certainrate of return may be expected, the valuation of individual holdings inthe portfolio can depart upward or downward from that expected rate ofreturn. This upward and downward variation from the expected value isknown as variance, or volatility. Over time, securities, in theory,should have an efficient frontier for expected volatility and return.According to theory, securities with a higher expected risk will have ahigher expected return.

Financial indices are often used to benchmark the performance of afinancial instrument. The S&P 500® Index is an example of one suchbenchmark for stock-oriented funds and the Barclays Aggregate Bond Indexis an example of a benchmark for bond funds. The S&P 500® is the largestequity benchmark in the world. Trillions of dollars are either investedin this benchmark or in funds benchmarked to it. Since yearend 1999,U.S. broad market indices such as the S&P 500® have experienced longperiods of underperformance. For example, an investor in the S&P 500® atyearend 1999 was down approximately 20% 10 years later in nominal termsat yearend 2009, depending on fees and treatment of dividends. It wasnot until late 2012 that the S&P 500® had a positive nominal return forthese yearend 1999 investors, including many large pension funds andendowments. As of October 2014, the S&P 500® had a negative real returnsince yearend 1999. During this same period, broad-based funds holdingU.S. government or corporate debt have had positive real returns withcorporate debt earning more than government debt during this period.This premium was due to the extra risk of a corporate bond versus a U.S.government bond of comparable duration. These markets had their annualfluctuations, but have been fairly stable; over a reasonable period oftime, these securities had both positive returns and differences thatwould be expected based on risk. Neither of these statements can be madefor equity indices such as the S&P 500® that lost value on an absolutebasis and underperformed materially over a long period of time withrespect to the less risky indices holding investment-grade corporate orgovernment debt.

Given a cap-weighted methodology, a change in the market value of arelatively large company has a disproportionate effect on an equityindex, while a change in the debt outstanding of a relatively highlyindebted issuer has a disproportionate effect on a fixed income index.Funds that track these indices also experience the correspondingfluctuations in value as the instruments representing the relativelylarger companies fluctuate in value.

The S&P 500®, like most broad market indices, iscapitalization-weighted. This means that the weight of an individualcompany in the index is proportional to its market capitalizationrelative to the other constituents. There are no controls in the S&P500® to ensure that a single security or groups of securities that sharea common risk do not become overweighted to represent too large aproportion of the portfolio. That is, the types of controls used inscientific fields and engineered processes where population controls areused to limit the influence that one part of a population can have on atotal population being measured are not used in the broad marketindices. Such controls limit both positive and negative influences. Inpopulation studies, controls are used to produce a normative model of anunderlying population. Because there are no controls in the benchmarkscurrently used to invest in equity securities, there is no assurancethat historical returns from yearend 1999 to the present arerepresentative of equity securities in general. The strategy ofcapitalization-weighting without controls has produced below-averagereturns for long periods of time.

The results of the major U.S. broad-market equity indices since 1999appear to be inconsistent with the main theories of the pricing ofinvestment securities and the theory of efficient markets. Much of thework on efficient markets and asset pricing followed the pioneering workof Markowitz and Sharpe with later notable additions by others such asFama and French. Their theories suggest that individual securities arepriced at a level that is expected to produce a risk-adjusted returnrelative to other investment securities and that, by following certainrules, a portfolio of securities has a higher probability than anindividual security of achieving this risk-adjusted rate of return inany given period or over several periods.

The principles that Markowitz and others proposed have been used toassist investors and managers in the selection of the most efficientportfolio design by analyzing various possible portfolios of a given setof securities. By delineating a portfolio construction process thatentails choosing securities whose risk-return profiles divergesignificantly, the models show investors how to reduce their risk. Thefoundational model in this area is known as the mean-variance modelbecause it is based on expected returns (mean) and the distribution fromexpected returns (variance) of the various portfolios. When developingthe original mean-variance model, Markowitz made the assumption that aportfolio that maximizes return for a given risk or minimizes risk for agiven return is an efficient portfolio. Thus, portfolios are selectedusing the following rules: (a) from the portfolios that have the sameexpected return, the investor will prefer the portfolio with lower risk,and (b) from the portfolios that have the same risk level, an investorwill prefer the portfolio with higher expected rate of return.

To facilitate portfolio construction, Markowitz used the expectedcovariance or correlation among securities as an additional input thatwould enable investors to maximize their risk-adjusted return at theportfolio level. Although an individual security may underperform for along period of time, the rules developed for efficient portfolioconstruction were designed to reduce, through diversification, thisprobability of underperformance with respect to the portfolio ofsecurities. According to these foundational theories, investors couldexpect to be compensated only for systematic, or broad-market, risks,with a premium commensurate with the risks of a given asset class, andshould be able to diversify away their exposure to non-systematic risksat the efficient frontier, consisting of the hypothesized marketportfolio.

One explanation for the inconsistency between modern portfolios and thetheoretical portfolios on which the efficient market hypothesis wasdeveloped is that modern portfolios operate at a much greater scale andlevel of complexity than the theoretical examples. The early theoreticalmodels based on the efficient market hypothesis and capital assetpricing model tend to use individual securities and describediversification within portfolios consisting of numbers of securitiesthat are in the single digits and low double digits. Many of thefoundational papers were written before the mutual fund boom of the1980s and 1990s following the creation of individual retirement accounts(IRAs) by the Employee Retirement Income Security Act (ERISA) of 1974,as well as the introduction of the first index fund in 1976. Forexample, the Markowitz paper on portfolio selection published in theJournal of Finance was written in 1952. According to the firstshareowner census undertaken by the New York Stock Exchange (NYSE) in1952, only 6.5 million Americans owned common stock at the time (about4.2% of the U.S. population), and each held an average of four stocks.Sharpe's paper, “A Simplified Model for Portfolio Analysis,” was writtenin 1963 and his book, “Portfolio Theory and Capital Markets,” waswritten in 1970, long before the mutual fund boom created by ERISA, theadvent of globalization and modern technology, the development ofexchange-traded products enabling retail investors en masse to holdthousands of securities at once, or the widespread recognition byinstitutional investors of the unique problems associated with managingsuch large funds.

Modern portfolios manage trillions of dollars in the aggregate. Thetotal investment into US mutual funds was $13 trillion dollars in 2012.In order to reduce exposure to non-systematic risks while avoidingrelatively illiquid positions, the portfolios require thousands ofsecurities in diverse risk groups. At this scale, lacking applicablefinancial theory to guide selections and weights, as portfolio theorywas developed for portfolios of a much smaller scale, building efficientportfolios has been challenging. The absolute scale of investment todayby very large institutions has grown exponentially since the mutual fundboom of 80s and 90s, discussed above. In addition, the underlyingpopulation of securities has grown in heterogeneity and complexity. Thisdiversity and interconnectedness is increasing every year. The need tocontrol for the non-systematic risks embedded in this portfolio ofcompanies also increases every year.

There is a need for a framework that enables the systematic comparisonand contextualization of all types of securities in today's complexheterogeneous global market. Specifically, there is a great need for aframework that enables systematic comparison and contextualization ofall types of equities in today's complex heterogeneous global market. Asystems approach to organizing economic and financial information wouldaccomplish this by enabling us to interrelate the vast data related tothese activities and analyze economic and financial interdependencies.

In addition, there is a need for a new normative methodology forconstructing portfolios of investment securities, one that addresses thecomplexities of today's companies and the increasing size and diversityof today's funds by applying the approach and foundational principles ofMarkowitz and Sharpe to the complexities of today's large-scale funds.

Some efforts for portfolio construction attempt to address the complexheterogeneous global market by relying on existing systems forclassifying companies. Current systems of classification, such as GlobalIndustry Classification Standard (GICS), are not well-suited forbuilding new models of potential efficient portfolios of theselarge-scale modern investment vehicles that draw upon complex andglobally interrelated universes of equities. The NAICS or GICS relatecompanies by their positions in a fixed hierarchy. There are twosignificant limitations of the fixed NAICS and GICS hierarchies: 1) anyitems without a common parent are unrelated and cannot be compared usingterms in the hierarchy; 2) any items sharing a parent can only becompared along the terms that GICS or NAICS uses to label that group(insofar as the names of the groups indicate the term that separatesthem, e.g., “consumer” versus “commercial” may relate to the customerbase).

These systems, similar to the foundational papers in finance, werecreated before the advent of large digital databases; they are modeledafter the frameworks of the time such as the Dewey Decimal System andStandard Industry Classification System. Those systems rely on a fixedhierarchy in which each entity has a single parent; that parent has asingle parent, and so on. Each parent has descriptions, but not conceptsof specific attributes that would enable an entity under one parent tobe related to an entity under another parent.

Without the ability in the data structure to relate an entity under oneparent to an entity under another parent, it is hard to understand themultivariate risks to which companies are exposed and, thus, to see howmany securities in a large portfolio or index may share a similar orrelated risk. The shortcomings of current classification systems arebecoming increasingly apparent given the complexities of today'scompanies and the increasing size and diversity of today's funds.Although many of the biggest risks in a capitalization-weighted strategyresult from the lack of controls for single risk exposures, bubbles, ormassive non-systematic price corrections, there are currently limitedtools to systematically address these problems. Thus, there is a needfor a multivariate attribute-driven categorization system enabled bycurrent data processing capable of providing these tools as well as theability to build multiple different portfolios to assess the efficiencyof each and test for a normative case.

Benchmarks

In addition to the systems used to organize securities and theinformation about them, modern portfolio construction is challenged byanother step of the process which has been slow to evolve: thebenchmarks against which to compare their performance. In other areas ofeconomics and finance, the role of benchmarks has been well established.Central banks routinely use inflation targets to guide policy, which hasproved instrumental in increasing the predictability of price changes.This has enabled consumers, merchants, and investors to consume, save,and invest with a high degree of confidence in near to medium-term pricechanges. National economic ministries routinely project their futureannualized GDP growth and seek to achieve it, which multilateralinstitutions, banks, and investors rely on as an index of a country'seconomic health.

In corporate finance, publicly traded companies regularly issue earningsguidance and have quarterly earnings targets, which it is the CFO'sprincipal role to achieve. Companies are benchmarked against theirearnings targets and held accountable for them by boards and financialanalysts, and even minor shortfalls in earnings frequently lead toprecipitous drops in stock price. CFOs are also expected to deliver ontarget returns on equity, which, since it is junior to debt in thecapital structure, has a higher cost of capital for a given company andshould have higher returns than the debt issued by a company. In eachcase, modern technology has enabled decision makers to more accuratelyforecast future economic and financial outcomes, control for risk, andachieve their benchmarks with a high degree of predictability.

At the portfolio level, however, there is no comparable accountabilityfor equity benchmarks. Since equity investments are riskier than debtinvestments at the portfolio level all equity indices should strive toearn a consistent premium to corporate long-term bonds. Just as allcompanies will expect a higher cost of equity than debt financing, allequity investors' indices, like the companies they invest in, shouldanticipate a higher return when they invest in a company's equity ratherthan its debt issuances. Because of the statistical properties of largesets of securities, investors should expect to see this risk premiumeven more consistently in an index portfolio. This risk premium shouldbe realized at the portfolio level; equity index investors should striveto beat corporate long-term bond returns for their constituent group ona consistent basis.

The capital asset pricing model uses the term alpha to describeoutperformance of a benchmark; from a company's perspective, generatingalpha entails beating its return projections. For any given company, anequity premium is commensurate with achieving earnings estimates andoutperforming borrowing rates. The same principle should hold at theportfolio and index level; investors in portfolios of equities shouldexpect returns that are higher than the average borrowing rate for thebonds of a given constituent group. If an index or portfolio does notachieve the performance target predicted by theory, a new methodology isrequired that will realize that target more consistently andpredictably.

The S&P 500 is widely accepted as an equity benchmark even while itcontinues to lack risk controls and exhibit higher volatility thanpredicted by theoretical models. It fails to achieve the rates of returnpredicted for it by the foundational finance theories and asset pricingmodels. Nevertheless, the methodology of the S&P 500 has not changedsignificantly since its inception, and it has failed to capitalize onthe tools of modern technology and data analytics to control for riskand achieve more predictable, reliable rates of return. Thus, there is aneed for a reconsideration of how to construct equity benchmarks and thestandards for them.

Conglomerates

Corporations have sought to achieve diversification at the company levelthrough the conglomerate form, which involves acquiring and managingmultiple independently operated and often functionally unrelatedbusinesses through a parent company. Owners of conglomerates sought toreduce the volatility in earnings associated with business cycles invarious industries by organizing relatively uncorrelated income streamsunder the same corporate structure; some also sought to achieve costsavings through synergies in procurement, branding, marketing, andsales, to avoid antitrust restrictions on expansion and consolidation ina particular industry by aggregating interests across multiple sectors.

Although conglomerates have enjoyed substantial popularity in certainwealthy countries following long periods of high economic growth—theU.S. in the 1960s, Japan in the 1980s, and more recently, SouthKorea—they have largely fallen out of favor in high-income markets. Theextra layers of bureaucracy and lack of sufficient industry expertise atthe holding or parent company level frequently have made conglomeratestoo complex to manage effectively.

More recently, private equity firms have sought to achieve similarobjectives to those of conglomerate managers by acquiring and managingmature businesses, frequently in a wide variety of industries. Thesignificant fees charged by such firms, typically comprising 2% ofassets managed and 20% of returns over a benchmark in addition todeal-specific fees, have impeded their ability, as a group, to generatehigh returns to investors, while other firms have foundered due tosimilar challenges that confronted conglomerates, failed to capitalizeon potential marketing, sales, and operational synergies, or incurredexcessive leverage that contributed to large losses during economicdownturns.

While some private equity firms consistently have shown very strongperformance, most of them are limited partnerships inaccessible to thegeneral public due to regulatory restrictions, and the informationregarding their operations, strategy, and investments is largely opaqueand frequently unavailable. The lack of transparency and liquidity inthese funds, as well as the challenges involved in managing businessesacross disparate sectors, have impeded the capacity of these firms toscale. At present, the largest traditional investment firm itselfmanages more capital than the entire global private equity industrycombined.

Volatility

Volatility in pricing refers to fluctuations in price. Volatility is asignificant factor in portfolio performance and these price fluctuationsmay create a drag on portfolio growth. For example, daily volatility hasbeen shown to hurt the return of leveraged exchange-traded funds. Randommovements in investment securities without controls at the portfoliolevel, especially large downward movements caused by unpredictableevents or the popping of non-systematic bubbles, reduce risk andliquidity-adjusted returns. In these cases, there is little to noexpectation that portfolios and their constituent investment securitieswill rebound to pre-existing levels. In both of these cases, thesecurities being impacted are being re-priced because of new informationor a sudden market recognition that they were overpriced.

In an effort to reduce the effects of volatility on a portfolio, variousweighting schemes have been proposed in the investment industry. Forexample, one method described in U.S. Pat. No. 8,306,892 operates bycalculating weights based on market capitalization, gross-domesticproduct, and geographic region. In another example, described in U.S.Pat. No. 8,131,620, weights in a portfolio of securities are based onmarket capitalization and dividend yield. Numerous other portfolioweighting schemes exist. However, none of these weighting schemes fullyaddress the shortcomings of prior art portfolio theory, as discussedabove. Some examples, such as that described in U.S. Pat. No. 8,005,740,use accounting-based metrics for weighting securities universes.

In prior art portfolio construction, random groups of securities arelikely to have periods of significant valuation swings, both up anddown, from one time period to another. These massive swings in value inrandom groups of securities may not be caused by variables such asaccounting attributes or their designation as “growth” or “value”stocks. The valuation swings could be caused by, for instance, companiesbeing long a specific commodity when the commodity suddenly loses itsvalue; over-exuberance in the demand prospects for a company's orindustry's product that does not meet demand; long fixed-cost contractswhen the actual costs available to their competitors changes;over-weighting of a certain asset in the product mix when that assetloses its value; or other idiosyncratic reasons.

There are many reasons for apparently random bubbles. In some cases,they are systematic or broad-market bubbles; in others, they are largelylimited to a constituent group (such as an asset class or industry).There are certain events that appeared to be systematic because theyimpacted index and portfolio returns so severely, such as the Internetbubble of the late 1990s, but are non-systematic. In either case, theimpact on an investor's returns when the bubbles collapse can beextremely negative as a result of portfolio biases and overexposure toconstituents that are especially impacted by the collapse of the bubble.

The random walk hypothesis in financial theory represents the inabilityto address the apparent randomness of volatility and returns inequity-based investment securities. The hypothesis implies that in anefficient market, a large random selection of equity-based investmentsecurities will perform as well as an actively-managed selection ofequity-based securities, before adjusting for taxes and fees. The randomwalk hypothesis is the underlying reason for the proliferation of indexfunds and the broad support for passive index funds by the academiccommunity. The hypothesis, taken to its logical extreme, suggests that ablindfolded monkey throwing darts at the stock listings could select aportfolio that would do just as well as one selected by the experts.

Many different weighting strategies have been proposed to deal with thisproblem of random volatility in equity-based investment securities. Therecent underperformance of these passive capitalization andeven-weighted indices to debt indices that track comparable universes ofcompanies has highlighted that these passive indices continually affirmthe same randomness hypothesis.

A major problem in the risk management of large portfolios of securitiesis the inability in existing systems to control for the occurrence ofthese types of events without a framework to define homogeneoussubpopulations. If a portfolio inadvertently over-weights in a securityor groups of securities that have a common bubble or bankruptcy risk,the returns can be materially impacted by a relatively small number ofsecurities in the portfolio. Non-systematic bubbles and bankruptcies areassociated with non-systematic factors of the industries, companies, orassets associated with specific investment securities. In several cases,over-weighting in specific non-systematic variables has causedsignificant negative impacts on a portfolio. This was clearly the caseof the Internet bubble. In calendar year 2000, thecapitalization-weighted S&P 500® was down 9.09%. In that year, therewere 16 stocks that were down 49.8%, while the rest of the market was up4.28%. Unfortunately for investors in funds tracking that index, these16 companies, which were all in the business of moving, storing, orprocessing information, comprised 24.8% of the total portfolio. Theunderperformance of these select securities had a massivelydisproportionate effect on the index, and the trillions of dollars infunds benchmarked to it, because of the lack of controls on theunderlying index.

Prior efforts to improve portfolio returns generally appear to have atleast three problems: 1) a sub-optimal number of groups; 2) insufficientability to control for covariance within groups or correlation amonggroups to ensure that each group operates in a predictablegroup-specific way; and 3) no way of defining a group in a systematicway that is applicable across an entire economy and permits all groupsto be related to one another. Existing large-scale heterogeneous indicesand portfolios of securities lack controls on their constituent groupsand neither capitalization-weighting nor even weighting are capable ofreducing the impact of group-specific risks at the portfolio level in apopulation of securities.

Covariance and Correlation

While finance theorists have made significant breakthroughs inforecasting the return and variance for individual securities, there hasbeen little advancement in finding reliable indicators of the pairwisecorrelations or covariances between securities, a required input to theMarkowitz model. In 1973, financial economists Edwin Elton and MartinGruber addressed why quantitative solutions are unlikely to bepracticable at scale, and noted that to obtain efficient portfolios fromamong 200 stocks, 19,990 correlation coefficients would have to beproduced.

There are also institutional impediments to finding generally applicableand sufficiently explanatory indicators, as there is highly unlikely toexist any individual at a financial institution sufficiently familiarwith the mathematical analysis of each constituent of a substantialequity universe to be able to approximate a quantitative solution. Eltonand Gruber concluded that there is no non-overlapping organizationalstructure that would permit security analysts in a financial institutionto produce estimates of correlation coefficients between all relevantpairs of stocks, since each analyst follows a subset of the stocks inwhich the institution has an interest.

In an effort to address the lack of reliable indicators of thecorrelation in how securities perform, traditional models such as thecapital asset pricing model (CAPM) assume that all residual pairwisecorrelations are zero. That is, it is assumed that each security has norelationship to any other security in excess of co-movement with themarket as a whole. This assumption lacks realism: a simple likelihoodratio test for zero correlations rejects the null hypothesis of zeroresidual pairwise correlations at the 0.000001 significance level.

Elton and Gruber illustrate that the CAPM can be improved upon simply byassuming a single nonzero pairwise correlation to be assigned across anentire portfolio, but acknowledge the severe limitations of thisapproach. The challenges referenced above, and the lack of awell-developed, field-specific framework to address the covariance issueat scale, have left the problem unsolved. The increasing scale,complexity, and heterogeneity of modern portfolios have made thischallenge more acute.

Purely quantitative measures of correlation have proven least accurateand least predictive precisely when they are most needed: duringbubbles, crashes, and other periods of high market volatility, whenthese measures have deviated far from their historical norms. Investorswho have sought to diversify principally based on quantitativehistorical covariances have sustained extraordinary losses during recentperiods of market volatility.

Factor Models

Asset pricing models such as the CAPM frequently have failed toaccurately describe or predict performance characteristics ofsecurities, groups of securities, or portfolios. These models isolate avery small number of factors believed to be driving security pricereturns and are predicated on the assumption that they can be determinedpurely quantitatively.

The CAPM relies on the risk free rate, the market return, and theidiosyncratic risk of the security; in other words, it is predicated onthe assumptions (among others) that there is one factor F common to allsecurities in the market, there exist a set of factors f_(1,2 . . . n)which map precisely, in a one-to-one correspondence, to the set ofsecurities s_(1,2 . . . n), that these factors and their weights areessentially stable over time, and that the relationship among thesefactors and their weights is entirely unknown.

The Fama-French three-factor model adds size and book to market value tothe aforementioned factors, while their posited five-factor model,which, as of November 2013, also adds profitability and asset growth,does not yet appear to improve on their previous model. (Eugene Fama andKenneth French, “A Five-Factor Asset Pricing Model,” working paper,September 2014.) Carhart's posited four-factor model adds momentum tothe three-factor model. (Carhart, M. M., “On Persistence in Mutual FundPerformance,” The Journal of Finance 52: 57-82 (1997).) Tobias Adrian,Emanuel Moench, and Hyun Song-Shin point to the systemic impact ofaggregate broker-dealer capital structure and asset growth innon-banking financial institutions on equity and bond prices. (TobiasAdrian, Emanuel Moench, and Hyun Song Shin, “Financial Intermediation,Asset Prices, and Macroeconomic Dynamics,” Federal Reserve Bank of NewYork, 2010.) Andrew Lo and Amir Khandani add common factors such asgeneral market volatility and commodity prices, and emphasize liquidityas an additional factor at the security level which was unduly neglectedin studies of large and mid-cap stocks in developed markets duringperiods of little turbulence, when liquidity factors are less relevant.(Andrew Lo and Amir Khandani, “Illiquidity Premia in Asset Returns,”draft paper, June 2009.)

Methodologies focusing first on quantitative analysis that have failedto identify any factors or risks other than systematic andidiosyncratic, or the relationship among the various idiosyncraticfactors or risks, and a lack of computing power when many of the keyparadigms of finance were formulated, have led portfolio and indexconstruction to be predicated on the assumption that all drivers ofsecurity price returns either a) affect every security in the entiremarket precisely the same way, or b) affect only one security in theentire market in any way at all. This untenable assumption has madeeffective portfolio and index construction extremely difficult.

Problems of Scale

For multiple reasons, the problems described above are particularlyacute in large-scale portfolios of securities. Various reasons whymanagement at scale is difficult are provided below.

(a) Charter limits on ownership: For many funds and fund managers, thereare limits on the percentage of a company they can own. For example, forany fund that seeks to acquire a 5% holdings of U.S. public equities,there are required 13-D filings and more extensive regulatory oversight.Many funds will not or cannot cross that threshold.

(b) Liquidity limits on ownership: The more a fund owns of an individualsecurity, particularly for large holdings, the harder it generally is tosell. The effect is frequently trivial for small dollar value holdingsin liquid securities, but may be significant for larger holdings orrelatively illiquid securities.

(c) Large funds need a large number of securities to fill out aportfolio: Due to the factors identified above as well as otherpractical issues, a large fund needs a large number of companies toinvest in due to liquidity and ownership issues. Across an economy,there are many linkages among companies, and the larger the number ofcompanies under evaluation, the more difficult it is to track andoversee the linkages and risks that come from them.

(d) Large funds may face a limited selection of securities: Due to thefactors identified above as well as many more practical issues, largefunds often need to invest disproportionately in large companies orother funds. The available companies in this group vary over time. Inaddition, these securities have variable weights and aggregatedifferently depending on what companies exist in which category at anygiven point in time.

(e) Geographic variation: In addition to changes over time, thisindustry, sector, or company selection varies by geography; in largeportfolios, indices, or funds comprised of securities, determining thegeographic exposure of assets, operations, and products, as non-limitingexamples, is impracticable using prior art methods. Sectordifferentiation may be a greater cause of price movements betweengeographies than the underlying currency that drives the products. Forexample, portfolios of US securities are often more heavily weighted intechnology stocks than portfolios of European or Latin Americansecurities. Europe and Latin America are relatively heavy inmanufacturing and financials.

If a fund, index, or portfolio manager's goal is currencydifferentiation, it is important to control for these sector variations.Not only understanding the different potential risk groups that exist atany given point in time and in any specific geography or category, butalso being able to control for these risks is difficult using currentlyor previously known techniques.

(e) Attribute and overconcentration risk are multi-dimensional: Singleand multiple attributes are helpful in distinguishing risks inindividual companies, but attributes that are clear on an individuallevel are lost in larger classification systems. These varied, yetcritical, attributes impacting security price returns are oftenaggregated into one technology metacategory in large-scale funds. Theexisting categories in current systems tend to be standardized on aglobal basis and do not permit differentiation among these attributesthat aggregate to characterize each metacategory. The inability torepresent linked multi-attribute risks is a significant limitation forexisting large-scale investment portfolios.

If portfolios, and large-scale portfolios in particular, are not bettercontrolled, and the linkages between companies are not well understood,non-systematic events can appear to have systematic impact. Examples ofnon-systematic events are provided below. Known and existingclassification systems do not address the underlying statistical causesfor the systematic impact of the volatility of the constituents oflarge-scale portfolios of securities. With improved controls, however,the impact of non-systematic events could be limited.

BRIEF SUMMARY OF THE INVENTION

Some embodiments of the invention can include systems and methods forusing a computing environment for algorithmically determining thecomposition of elements in a functional system represented inn-dimensional space, the system or method comprising: electronicallystoring a set of data entities in a database system, the data entitiescorresponding to elements of a functional system, wherein the functionalsystem comprises a group of elements ordered by their functional rolesin a process converting inputs to outputs; electronically assigning oneor more functional attributes to an element corresponding to a dataentity in a logical data model that comprises at least two fieldsordered by a set of interrelationships among at least two elements inthe underlying functional system, the interrelationships correspondingto the functional properties of a process converting a set of inputelements to a set of output elements; assigning an m-dimensional arrayof n-dimensional tensors to the data entities, wherein a plurality ofentries in the array are based on the attributes of the elements andcorrespond to functional roles of the elements in a process convertinginputs to outputs; algorithmically determining a reference distributionD, wherein the reference distribution comprises the proportionalallocation of elements into a finite set of categoriesC=c_(1,2 . . . p); using a statistical test T to assess the relativeallocation of a set of data entities according to the referencedistribution; selecting an instance of a target distribution D′, whereinthe target distribution comprises an algorithmic proportional assignmentof data entities into a finite set of categories C′; and executing astatistical test T′ to assess the relative allocation in functionalspace of a set of data entities according to the target distribution.

Further embodiments of the system or method can include: assigning ascoring matrix of dimensionality ≤m×n comprising a set of weightsassociated with the m-dimensional array of n-dimensional tensors;wherein the set of weights modifies the allocation of a variable acrossn-dimensional space so as to adjust the distribution, as determined bythe statistical test; and periodically rebalancing the set of weightsassociated with the tensors based on changes in the functional system.

Further embodiments of the system or method can include: receiving thescoring matrix; adding a k-dimensional set of n-dimensional tensors tothe m-dimensional set of data entities; using a machine learningtechnique to determine the new set of scores based on the locations ofthe tensors, the statistical test T′, and the target weight; andoutputting a matrix of dimension ≤(m+k)×n; wherein the entries of thematrix comprise updated scores of the tensors and dimensions.

Further embodiments of the system or method can include: receiving thescoring matrix; subtracting a set of size k of n-dimensional tensorsfrom the m-dimensional set of data entities; using a machine learningtechnique to determine the new set of scores based on the location ofthe tensors, the statistical test T′, and the target weight; outputtinga matrix of dimension ≥(m−k)×n; wherein the entries of the matrixcomprise updated scores of the tensors and dimensions.

Further embodiments of the system or method can include: selecting a setS=s_(1,2 . . . k) of size k and dimension ≤m of n-dimensional tensorsdefined by their functional distance; wherein functional distance is ameasure of the relative remoteness of data entities in functional space;computing the difference between S and the remaining set of dataentities L resulting in a matrix M′ of dimension ≥(m−k)×n; wherein theset of data entities in C are more functionally related than anarbitrary sample of data entities in S, as determined by a test ofstatistical significance; and using a statistical measure of relatednesson M′ to determine correspondence among functional and non-functionalattributes in M′, thereby increasing the analytical performance comparedto a non-filtered test on L.

Further embodiments of the system or method can include: using thescoring matrix as an input to a machine learning technique to constructa probability space where a functional location of a tensor maps to alocation with a corresponding probability for a plurality ofcategorizations; using the matrix representation of that coordinatespace to predict, with a given probability, where a data entity will beplaced into a category C; outputting an updated scoring matrix ofdimension m′×n′.

Further embodiments of the system or method can include: using thescoring matrix as an input to a machine learning technique to constructa coordinate space where tensors' functional locations form clustersbased a plurality of categories; using the matrix representation of thatcoordinate space to predict, with a given probability, where a dataentity will be placed into a category; outputting an updated scoringmatrix of dimension m″×n″.

Further embodiments of the system or method can include: D=D′; C=C′; anT=T′.

Without both a reliable and validated classification system usingfunctional attributes as well as a computerized system that uses astratified or segmented (or blocked) composite structure, prior artsystems are unable to control for the different attributes associatedwith the securities. A stratified or segmented composite portfolio canbe formed by selecting a group of investment securities, segmenting thesecurities into sub-groups according to attributes that correlate to oneor more identified investment security risks, and assigning portfolioweights to one or more sub-groups based on their stratified or segmentedpositions. The attributes can be selected from a universe of possiblevalues. Further positive and negative biases can be applied at anyarbitrary point, stratum, or segment, including to individual investmentsecurities, groups of arbitrarily selected investment securities, orarbitrary positions in the architecture.

The specific functional attributes associated with the investmentsecurities can be used to segment, stratify, and weight the holdings ofinvestment securities in a portfolio by assigning specific weights tothe risk groups in which the underlying securities are held in order tomeet the engineered risk objectives of the overall portfolio. As anon-limiting example, one of the goals in segmenting or stratifying riskgroups may be to reduce the impact of attribute-specific volatility dragon the portfolio as a whole. As non-limiting examples, the systems andmethods described herein can be used in investment management bycontrolling for specific types of random events that impact the overallrandomness of risk, return, skewness, and kurtosis in large portfoliosor groups of investment securities.

Multi-attribute risk composites can provide a tool to manage risk byreducing or minimizing the potential risk resulting from theseattributes and/or increasing or maximizing the potential return fromthese type of risks by engineering the composite to take advantage of anevent a manager expects to happen.

In some embodiments, a stratified composite portfolio can be created bytagging securities with risk attributes based on functional attributesand applying a weighting scheme that limits the exposure to individualattributes. The result of this process is a weighted portfolio thatstratifies or segments risk exposure across a number of risk attributecategories, and disperses the risk in the individual groups andsub-groups according to attribute categories within groups, to achieve adesired risk profile that can be represented by a target score.

In one aspect of the disclosure, there is provided acomputer-implemented method for storing a representation in a databaseof an index or portfolio of investment securities, the method comprisingelectronically storing one or more data entities in a database system,each of the data entities representing the identity of an investmentsecurity, the investment security associated with a correspondingeconomic entity; electronically tagging each data entity with one ormore functional attributes of the corresponding economic entities;wherein the functional attributes characterize the roles of each of theeconomic entities in one or more processes converting inputs to outputs;selecting multiple investment securities represented by the dataentities for inclusion in an index or portfolio of investmentsecurities; defining at least a first group and a second group ofinvestment securities based on the electronic tags or the functionalattributes associated with the corresponding economic entities;segmenting the selected investment securities into the two or moregroups based on the electronic tags or the functional attributes;wherein the investment securities in the first segmented group share afirst common or proximate functional attribute, and the investmentsecurities in the second segmented group share a second common orproximate functional attribute; electronically accessing the databaserepresentation of the segmented groups; electronically iterating throughthe accessed representations to compute a negative or positive weightfor one or more of the investment securities based on the one or moresegmented groups into which the investment securities are segmented; andassigning the negative or positive weight to the one or more of theinvestment securities; and electronically storing the assigned weight inthe database system.

Further embodiments include selecting one of the segmented groups ofinvestment securities which share a first common or proximate functionalattribute; segmenting the selected group of investment securities intotwo or more sub-groups, wherein the sub-groups are subsets of thesegmented groups; weighting the two or more segmented sub-groups;wherein the investment securities in a first sub-group share a thirdcommon or proximate functional attribute and the investment securitiesin a second sub-group share a fourth common or proximate functionalattribute.

In further embodiments, the joint intersection of each set of groups isthe empty set; and the joint intersection of each set of sub-groups isthe empty set. In further embodiments, one or more groups, sub-groups,or investment securities are weighted based on syntactic or functionaltags, or syntactic or functional attributes; and one or more groups,sub-groups, or investment securities are weighted based onnon-syntactic, non-functional tags, or non-syntactic or non-functionalattributes.

Further embodiments include assigning a target weight to a group,sub-group, or investment security; and periodically rebalancing thegroup, sub-group, or investment security to the target weight.

In further embodiments, one or more portfolios, indices, groups,sub-groups, or securities, or the data entities representing them, arerepresented in graphical, sequential, clustered, or networked form.

Further embodiments comprise electronically using predictive analyticsbased on functional attributes to forecast the performance, volatility,liquidity, variance, expected return, alpha, Jensen's alpha, beta,variance, covariance, semivariance, semideviation, correlation,autocorrelation, Sharpe ratio, Sortino ratio of one or more portfolios,groups, sub-groups, or investment securities, or the excess or residualof any of these metrics.

In further embodiments, one or more weights are assigned to aninvestment security based on a functional attribute of a correspondingeconomic entity, or electronic tag representing such an attribute.

Further embodiments comprise transmitting, sending, or relayinginformation regarding one or more data entities and one or more weightsto an exchange, index provider, index calculator, brokerage, assetmanager, investment advisor, investment manager, specialist,broker-dealer, authorized participant, trader, financial professional,investment professional, investor, general partner, limited partner,private equity investor, venture capital investor, hedge fund investor,conglomerate manager, executive, pension fund advisor, endowmentmanager, fund manager, or securities trading platform.

Further embodiments comprise using one or more weights to construct anindex, buy, sell, issue or transmit an order, or execute trades in aninvestment security, group, or portfolio.

In further embodiments, the functional attributes are associated withrisk exposures, and wherein at least two groups of investment securitiesare associated with different functional attributes and different riskexposures.

Further embodiments comprise associating two or more numerical valueswith two or more groups, tags, attributes, risk exposures orrelationships, wherein the numerical values relate to economic,financial, or capital markets-based data; associating a statisticalproperty, selected from among mean, variance, standard deviation, skew,kurtosis, correlation, semivariance, and semideviation, with thosegroups, tags, attributes, risk exposures, or relationships based on thenumerical values; calculating two or more statistical values associatedwith the statistical property; determining the statistical significanceof the calculated statistical values of each group, tag, attribute, riskexposure, or relationship; validating that the statistical values aresignificant at a predetermined level; and if the values are notsignificant, reassigning groups, tags, attributes, risk exposures, orrelationships.

In further embodiments, the number of securities in each group is chosensuch that a statistical power of the statistical test exceeds apredetermined level.

In further embodiments, the investment securities or groups are selectedfrom among equity, debt, derivatives, currencies, commodities, funds,notes, alternative investments, exchange-traded products, real assets,and structured products.

Further embodiments comprise selecting a financial or economic metric tomeasure with respect to one or more of the groups, indices, orportfolios, wherein: the distribution of expected or realized values ofthe metric for the index, portfolio, or group is relatively more normalthan the distribution of expected or realized values of the metric foran alternative index, portfolio, or group; or the value of the metric ismore stable or predictable for the index or portfolio than it is for thegroup, as measured by a mathematical test of stability orpredictability; or the value of the metric is more stable or predictablefor the group than it is for an investment security, as measured by themathematical test of stability or predictability. In some furtherembodiments, the normality of the distribution is assessed usingCramdr-von Mises criterion, Kolmogorov-Smirnov test, Shapiro-Wilk test,Anderson-Darling test, Jarque-Bera test, Siegel-Tukey test, Kuiper test,p-value test, a Q-Q plot, a test of skewness, or a test of kurtosis. Asnon-limiting examples, stability may be assessed through a test ofvariance or a test of heteroscedasticity.

Further embodiment comprise electronically storing one or more dataentities, each of the data entities representing the identity of asegmented group, the segmented group comprising one or more investmentsecurities and associated with one or more corresponding economicentities; and electronically tagging each group with one or morefunctional attributes of the corresponding economic entities.

Further embodiments comprise identifying an index or benchmark to track;selecting, grouping, or weighting the investment securities so as totrack substantially or replicate the performance of the identified indexor benchmark.

In further embodiments, the portfolio or index comprises a syntheticconglomerate.

In further embodiments, one or more weights are assigned based onsemantic, syntactic, morphological, morphosyntactic, anatomical,physiological, functional, graphical, or value chain proximity.

Further embodiments comprise electronically storing a computerizedrepresentation of an economic systems syntax, wherein the economicsystems syntax can be applied by a computer processor to establish thevalidity of expressions of elements of the system based on one or morefunctional properties of the economic entities.

Further embodiments comprise recommending a portfolio, group, orinvestment security to a user based on functional attributeselectronically identified by the system or user.

Further embodiments comprise arranging the selected data entities into astratified structure including at least two parent groups and at leasttwo sub-groups of each parent group such that: one or more parent groupsare defined by one or more functional attributes such that data entitiesof those parent groups have in common the attributes that define thoseparent groups and wherein at least two parent groups are associated withdifferent risks; the sub-groups inherit one or more functionalattributes and corresponding risks from the parent groups; and thesub-groups are defined by one or more divergent functional attributessuch that one or more sub-groups are associated with different risksfrom the parent groups and from other sub-groups.

Further embodiments comprise calculating a measure of statisticaldependence between each of two parent groups and between each of twosub-groups; determining whether the parent groups and sub-groups haverelatively high intra-group statistical dependence; determining whetherthe parent groups and sub-groups have relatively low inter-groupstatistical dependence; and if the intra-group statistical dependencedoes not exceed the inter-group statistical dependence, reorganizing thegroups or sub-groups.

In further embodiments, one or more sub-groups are assigned weightsrelative to one another according to a weighting scheme such that theweight of one or more parents equals the sum of the products that resultfrom multiplying a sub-group by its assigned weight according to theweighting scheme.

In further embodiments, the realized returns of the portfolio exceedthose of a commercially available index or benchmark, over the previousone, three, and five years for a given level of risk, or match those ofthe index or benchmark at a lower level of risk; wherein the securitiesin the portfolio are the same, or substantially the same, as thesecurities in the index or benchmark.

In another aspect of the disclosure, there is provided acomputer-implemented method for storing a database characterization ofan index, portfolio, set, aggregate, or composite of elements of afunctional system, or of a representation of those elements, the methodcomprising: electronically storing a set of data entities in a databasesystem, each of the data entities corresponding to an element of afunctional system; wherein the functional system comprises a group ofelements ordered by their functional roles in converting inputs tooutputs, or as the inputs, or as the outputs; electronically assigningeach data entity associated with an element one or more functionalattributes represented as an electronic tag; wherein the functionalattributes characterize the roles of each of the elements in a processof converting inputs to outputs; selecting multiple elements, or arepresentation of those elements, characterized by data entities forinclusion in a portfolio, index, set, aggregate, or composite;segmenting the selected elements, or a representation of those elements,into two or more defined groups based on the electronic tagsrepresenting the functional attributes associated with the correspondingelements; wherein the first group shares a first common functionalattribute, and the second group shares a second common functionalattribute; electronically accessing the database representation of thesegmented groups; electronically iterating through the accessedrepresentations to compute a negative or positive weight for one or moreof the elements, or a representation of those elements, based on the oneor more segmented groups; and assigning the negative or positive weightto the one or more of the elements, or a representation of thoseelements; and electronically storing the assigned weight in the databasesystem.

In further embodiments, the functional system is economic; the elementscomprise one or more inputs, outputs, resources, activities, functions,businesses, enterprises, jobs, companies, projects, products, assets,shareholder's equity, liabilities, commodities, currencies, imports,exports, communities, or economic interests in, or collections of, anyof the foregoing, in the economic system; investment securitiesrepresent the elements of the economic system; wherein one or moreinvestment securities, or one or more groups, are selected from amongequity, debt, derivatives, currencies, commodities, funds, notes,alternative investments, exchange-traded products, real assets, andstructured products; and one or more data entities identify one or moreinvestment securities.

In another aspect of the disclosure, there is provided a system forexecuting a command in a computing environment to construct arepresentation of an index or portfolio of investment securities in adatabase, the system comprising: a computerized processor configuredfor: electronically tagging one or more data entities with one or morefunctional attributes of the corresponding economic entities; whereinthe functional attributes characterize the roles of each of the economicentities in one or more processes converting inputs to outputs;selecting multiple investment securities represented by the dataentities for inclusion in an index or portfolio of investmentsecurities; defining at least a first group and a second group ofinvestment securities based on the electronic tags or the functionalattributes associated with the corresponding economic entities;segmenting the selected investment securities into the two or moregroups based on the electronic tags or the functional attributes;wherein the investment securities in the first segmented group share afirst common or proximate functional attribute, and the investmentsecurities in the second segmented group share a second common orproximate functional attribute; electronically accessing the databaserepresentation of the segmented groups; electronically iterating throughthe accessed representations to compute a negative or positive weightfor one or more of the investment securities based on the one or moresegmented groups into which the investment securities are segmented; andassigning the negative or positive weight to the one or more of theinvestment securities; and an electronic data store configured for:electronically storing the one or more data entities in a databasesystem, each of the data entities representing the identity of aninvestment security, the investment security associated with acorresponding economic entity; electronically storing the assignedweight in the database system.

In further embodiments, the computerized processor is further configuredfor: selecting one of the segmented groups of investment securitieswhich share a first common or proximate functional attribute; segmentingthe selected group of investment securities into two or more sub-groups,wherein the sub-groups are subsets of the segmented groups; weightingthe two or more segmented sub-groups; wherein the investment securitiesin a first sub-group share a third common or proximate functionalattribute and the investment securities in a second sub-group share afourth common or proximate functional attribute.

In further embodiments, the computerized processor is further configuredfor: selecting one of the segmented groups of elements, orrepresentations of those elements, which share a first common orproximate functional attribute; segmenting the selected group ofelements, or representations of those elements, into two or moresubgroups, wherein the subgroups are subsets of the segmented groups;wherein the groups of elements, or representations of those elements, ina first subgroup share a third common or proximate functional attributeand the elements, or representations of those elements, in a secondsubgroup share a fourth common or proximate functional attribute.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example method for creating a stratified compositeportfolio and weighting investment securities.

FIG. 2 illustrates an example method for creating a stratified compositeportfolio and weighting investment securities.

FIG. 3 illustrates an example stratification with three levels.

FIG. 4 illustrates an example data set consistent with the examplethree-level stratification.

FIG. 5 illustrates an example method for creating a stratified compositeportfolio and weighting investment securities.

FIG. 6 illustrates an example method for calculating weightings for astratified composite portfolio.

FIG. 7 illustrates an example method for creating a stratified compositeportfolio with a target score.

FIGS. 8A-8B illustrate an example architecture represented as statementsdefining an architecture and barcode.

FIG. 9 illustrates example relationships between syntax elementsgraphically.

FIG. 10 illustrates an example database implementation for the system.

FIG. 11 illustrates an example computerized system for stratifiedcomposite portfolio weighting.

FIG. 12 illustrates an example ordered set of fields showing examplerelationships among fields.

DETAILED DESCRIPTION Definitions

Investment Security: As used herein, an investment security is definedas a financial instrument that can represent, as non-limiting examples:an ownership position in a corporation (stock), a commodity, or acollection of assets; a securitized creditor relationship with aninstitution, such as a corporation, multilateral, or governmental bodysecured directly or indirectly by the assets of the issuer (bond);potential rights of purchase, sale, or ownership as represented by anoption or other derivative instrument; a security interest in acommodity or real asset, including, as non-limiting examples, energy,timberland, and precious metals; a group of other securities pooled intoa security, including, as non-limiting examples, a fund, exchange-tradedfund, exchange-traded product, or structured product; or any collectionthereof. A security may be a fungible, negotiable, financial instrumentthat represents a type of financial value associated with an economicentity. The company or economic entity that issues the security is knownas the issuer. The value of the security value can be based on the typeof security, the type of relationship with the issuer, and the type ofassets and liabilities that are directly or indirectly associated withthe security.

Economic entity: As used herein, an economic entity is involved in somecapacity, whether active or passive, in the production, distribution,trade, or consumption of real or virtual goods or services. Asnon-limiting examples, an economic entity may be a corporation, company,enterprise, business, work group, department, laborer, input, output,resource, activity, function, project, product, assets, liability,commodities, currencies, imports, exports, community, job, worker,individual, governmental body, intergovernmental organization,multilateral organization, non-governmental organization, socialenterprise, charity, non-profit, or any collection thereof. Asnon-limiting examples, an economic entity may pursue financial,environmental, social, or governmental objectives, or some combinationthereof.

Functional Attributes: The economic entities represented by theinvestment security can be associated with or have attributes.Functional attributes characterize the roles of the economic entities inprocesses converting inputs to outputs. The database system can operateon multiple types of attributes associated with an entity. Asnon-exclusive examples, the database system can operate on classes ofattributes that are: (a) relative, and/or (b) functional, and/or (c)contextual; and/or (d) absolute. Relative attributes may be, forexample, syntactic attributes, geographic, temporal, scoring systems,designations as high/low volume securities or as growth/valuesecurities.

In some cases, attributes can be defined to include attributes relatingto the entity associated with the security and correspondingly excludeattributes of the security itself. For those embodiments, the databasesystem can be configured to define attributes so as to specificallyexclude attributes relating to the type of investment security, such asequity, debt, or derivative, and characteristics of the investmentsecurity, such as preference, maturity, duration, or strike price. Inthose configurations, those excluded attributes are not considered to befunctional attributes because the included attributes relate to theeconomic entity with which the investment securities are associated, notthe security itself.

In some embodiments, functional attributes can be applied to inputs,outputs, or functions transforming inputs to outputs. In otherembodiments, functional attributes may apply to activities, resources,systems, subsystems, composites, or elements. As non-limiting examples,types of functional attributes can be: syntactic attributes or semanticattributes. Examples of functional attributes may include, asnon-limiting examples: (a) attributes related to investors (e.g.“institutional” v. “non-accredited”), (b) attributes of assets belongingto the company (e.g., “outsourced” vs. “in-house” for a manufacturingcompany), (c) attributes related to the product (e.g., “raw material”vs. “simple component”), (d) attributes related to customers (e.g.,“business” v. “consumer” v. “government”), and (e) attributes related tosuppliers (e.g. “wholesale” v. “retail”). The system can recognize anycombination of different types of attributes. Some attributes may havequalities that are both relative-to-universe and functional.

In some embodiments, functional attributes can be defined to excludeaccounting and performance-based attributes. In some embodiments, thefunctional attributes can be qualities, features, properties, orinherent characteristics of the underlying entity or assets with whichan investment security is associated. Functional attributes may definerelationships throughout the value chain and structure of an economicentity, including, as non-limiting examples: (a) what a company does,such as manufacturing or transportation; (b) aspects of the company'sproduct, such as specific utility provided by the car, computer orcouch; (c) what the company's customer does, such as consumer sales orbusiness intelligence; (d) what the customer's customer does; (e) theproducts and materials a company uses to provide its product; (f) themultivariate industries or industry segments in which a company mayoperate; (g) the structure of a company's business, such as integrated,non-integrated, forward integrated, backward integrated or networked;(h) risks based on a company's management, including its decisions andstrategies; (i) risks based on the internal operations of a company.

A major part of the linkages in an economic system are due tonon-systematic functional attributes associated with, as non-limitingexamples, a company's suppliers, products, industry, and operations, andgeographic location. Without a comprehensive awareness of such sharedattributes or linkages, it is very easy for portfolios with a largenumber of securities to become over-concentrated in non-systematic riskcategories.

At any given point in time, any one of these attributes or an industryevent related to these attributes may affect the risk associated withsecurities associated with entities that have these attributes.Understanding different potential risk groups and controlling for themis difficult without both a reliable and validated system of functionalattributes as well as a stratified or segmented composite architectureto control for the different attributes.

Functional attributes may be syntactically or semantically structured;they can be framed in natural or symbolic, relational language using themethods described in U.S. patent application Ser. No. 14/216,936, thecontents of which are hereby incorporated by reference herein. Anycombination of multiple attributes can be formed as a compoundattribute. Compound attributes can be defined as a new single attribute.

Stratified Composite Unit: As used herein, a stratified composite unitis defined as a stratified set of investment securities comprising: 1) aparent group that is defined by one or more attributes where all membersof the parent group have in common the attributes used to define theparent group; and 2) at least two sub-groups of the parent group, whichmay be considered to be children of the parent group and/or siblings ofeach other. All members of a sub-group have in common the attributesused to define the sub-group and its parent group. Any stratifiedcomposite unit and its constituent sub-units can include an arbitrarynumber of other sub-units that follow the rules of its parent unit orsub-unit. In some cases, a stratified composite unit may be comprised ofonly a parent group and two sub-units. In other cases, a stratifiedcomposite unit may be comprised of as many parts as the size anddiversity of the original parent will support. With reference to FIG. 4,a stratified composite unit can comprise elements 1210, 1230, and 1235.

Segmented Composite Unit: As used herein, a segmented composite unit isdefined as a segment for securities comprising: 1) a segmented groupdefined by one or more shared attributes; 2) at least two sub-segmentsof the larger segmented group, each of which contains constituentsecurities that share at least one attribute in common with one anotherand with the larger segment group. In some cases, a segmented compositeunit may be comprised only of a larger segmented group and twosub-segments. In other cases, a segmented composite unit may becomprised of as many segments as the size and diversity of the largersegment will support. A security may be a constituent, in whole or inpart, of one or more larger segmented composite units. With reference toFIG. 4, a segmented composite unit can comprise elements 1205 and 1210.

Stratified Composite Portfolio: As used herein, a stratified compositeportfolio is defined as comprising at least two stratified compositeunits wherein the attributes of the parents in the composite unitsrepresent risk groups such that: 1) parent risk groups havedifferentiable risk profiles; and, 2) the sub-units comprisinginvestment securities in risk groups are formed as stratified compositeunits.

Segmented Composite Portfolio: As used herein, a segmented compositeportfolio is defined as comprising at least two segmented compositeunits wherein the attributes of the larger composite units representrisk groups such that: 1) larger risk groups have differentiable riskprofiles; 2) the sub-segments comprising securities in risk groups areformed as segmented composite units.

While there may be other qualifications to be in the parent or largergrouping of a stratified or segmented composite unit, respectively,composite unit parents can satisfy the condition of sharing a specificcommon attribute or sets of common attributes with the members. A parentgrouping of the multiple stratified composite units can comprise astratified composite portfolio defined to create a portfolio ofcomposite units so that a defined differential risk is addressed by thecomposite units that comprise the stratified composite portfolio.

Portfolio: A portfolio, as used herein, can be any form or collection ofinvestment securities held by an investment company, institution, orindividual.

Introduction to Risk

Investments are made with an expectation of appreciation, or return, andof potential risk, or variance of these returns. The two measures arelinked: at a given level of liquidity, the higher the expected risk, thehigher the expected return. Stated differently, all else being equal,higher levels of risk should be compensated for by higher levels ofreturn. The probability of return is linked to the expected variance ofoutcome for a given security. The actual return expected for a securitymay be tied to many factors including market conditions, a given supplyof investment capital, or an expectation of inflation or deflation. Forexample, identifying that a company is in the semiconductor business isa differentiable risk. Furthermore, the type of semiconductor (e.g.,storage, processing, linking) is important, as are the raw materialsrequired and the identities of the customers.

Securities vary in their return characteristics and expectations.Certain types of securities represent a specific ownership position in aspecific company. Each type, such as a bond, an equity instrument, or aderivative, has its own specific ownership and investmentcharacteristics. The expected return from a security is based on thetype of security and its characteristics and the underlying performanceof the associated entity relative to the ownership represented by thesecurity. For any security, the expected return and the actual returnmay be materially different. Theory and empirical results alikeillustrate that divergence at the security level is substantially higherfor equities than relatively safe fixed-income instruments such asinvestment-grade government bonds.

An investment security's expected rate of return (and volatility)depends on factors including both market forces and forces tied to thespecific investment security and its underlying properties. The formerforces are systematic and impact broad classes of securities. The latterare specific and unique to each specific investment security, being tiedto the attributes of each specific investment security or groups ofsecurities. The variance of investment security returns that are tied tothe latter are tied to attributes of the specific securities which areshared in numerous segments of heterogeneous populations.

Risk can be associated with the qualities or attributes of the entitywith which the security is associated. The changes in fortunes or evenbankruptcy of a specific business are related to the functionalattributes of the business itself. These include any number of factorsincluding the business, its operations, its products, its customers, itscustomer's customer, the availability of supplies, the strength of theirsuppliers or the specific assets or liabilities of the business. Eventsrelated to any one of these things or any combination of these thingscan cause the fortunes of a business to change and, in so doing, changethe expected return of a business associated with a security.

In addition to an individual company, a portfolio of securities can beimpacted by these non-systematic risks if the portfolio is over-exposedor over-concentrated in a specific non-systematic risk. One of theprincipal reasons for having a portfolio is to reduce this exposure tonon-systematic risk by spreading it out over a number of investmentswith unique or disparate non-systematic risks such that no onenon-systematic risk will materially change the fortunes or expectedreturn of the overall portfolio. This strategy is relatively easier foran individual investor who can diversify a portfolio over a relativelysmall number of individual securities in relatively small amounts.However, this strategy has proven elusive for large-scale investors suchas pension funds or endowments that have billions of dollars (or dollarequivalents) to invest. Those large-scale investors must invest inhundreds or thousands of securities at any given point in timerepresenting billions of dollars of value. For investors with that scaleof investment, minimizing the impact of non-systemic risk factors in aportfolio has proven very difficult; they tend to overweight in largeindustry bubbles and are negatively affected by repeated technology orcommodity bubbles and continual over-weighting in large bankruptcies orlarge downgraded classes of financial instruments such asmortgage-backed securities or sovereign debt. The invention disclosedherein provides a method for portfolio managers to systematicallycontrol for these non-systematic portfolio risks that disproportionatelyand negatively impact large-scale portfolios.

Functional Attributes

Functional attributes can be used in the multi-attribute weightingscheme described herein. The systems described herein can operate byassigning one or more attributes to companies associated with aninvestment security. The methods described herein can be implemented ona computing device to group a portfolio of investment securities intosubsets using the functional attributes related to their associatedcompanies, commodities, assets, or liabilities. These attributes can beused as markers for the specific risks associated with events such asbankruptcy or market crashes. These attributes enable a portfoliomanager to stratify, segment, or sub-divide a portfolio into groupsaccording to attributes, where each group represents a specificattribute-related risk. When constructed in a stratified form, thechildren of these parent groups have both unique risks between groupsand share common risks with their parent.

After stratifying or segmenting a portfolio, weights can be assigned tothe units and a plan to reconstitute the weightings on a systematicbasis can be executed. In this way, a portfolio manager can understandand manage the specific risks in the portfolio. Additionally, risklevels can be engineered by arbitrarily setting weights for thestratified units. In some embodiments, the manager can determine thedesired risk at the beginning of the process, using these to form amulti-level hierarchy of distinct groups and sub-groups, and thenweighting the groups according to a desired risk outcome. In otherembodiments, the groups are used to form non-hierarchical segments,clusters, or groupings and then weighted according to a desired riskoutcome.

The methods described herein enable the calculation and implementationof weighting schemes for portfolios and their constituent securities,each of which have specific properties that are different from those ofuncontrolled portfolios of the same securities based on security orgroup-specific attributes. As described in more detail below, theinvention uses a set of security-specific functional attributes that aresyntactically and semantically related to constituents to reduce theportfolio-level effects of the randomness of individual security returnsby building portfolios of securities that reduce the impact of the risksassociated with functional attributes. It does so by stratifying andsegmenting the attributes and their risks in a controlled manner over acontrolled portfolio of population groupings, representing groupingsdefined by common attributes and groupings containing specificsecurities that share the attributes associated with the grouping.

FIG. 12 illustrates an example ordered set of fields showing examplerelationships between the fields according to the invention. Field 1205(“1”) is a defining association of Field 1210 (“1”), which jointlydescribe an action performed upon an object described in Field 1215(“C”). Fields 1220 and 1225 (“1” and “2” respectively) describe asequential step of the action described in Fields 1205 and 1210, anddescribe an action performed upon an object described in Field 1230(“C”). Field 1235 (“1”) is a sequential step of Field 1220 (“1”), and isa defining association of Field 1240 (“3”), which jointly describe anaction performed upon an object described in Field 1245 (“B”). Asdiscussed herein, a functional proximity algorithm can be configured tocompute correspondence based on the magnitude and category ofrelationships among a plurality of the data entities, such as thatbetween Field 1225 and Field 1215, or Field 1225 and Field 1205.

Stratification and Segmentation

To control for non-systematic risk, a portfolio manager must control forthe specific set of business risks that exist in any portfolio. Theserisks could be, among other things, company-related, industry-related,product-related, customer-related, or supplier-related. The larger aportfolio becomes, the more difficult it is for a portfolio manager tounderstand its exposure to specific non-systematic risks. The methods ofrisk group stratification described herein reduces the negative impactof attribute-specific volatility on the portfolio as a whole.

The systems described herein can be used to create a stratifiedarchitecture or segmented sets of specific risk groups, allocating thesecurities in a portfolio across these stratified or segmented riskgroups and selecting the desired exposure to the risk groups by applyingcalculated or user-provided weights for identified non-systematic risks.Thus, stratification or segmentation can be used to systematicallycontrol exposure to non-systematic risks. These exposures can then bemanaged over time by creating rebalancing rules that reset on anappropriate periodic schedule a portfolio's exposure to these identifiednon-systematic risks. In this way, a large-scale securities portfolio'sexposure to a set of non-systematic risks can be systematicallydetermined and managed.

The systems can include a programmable coordinate-guided system toproduce computer-generated risk groups and programmable assembly ofcomputer-generated risk groups into computer-generated portfolios ofthese risk groups each containing securities that match the attributesof the specific group.

Economic entities with one or more common functional attributescorrelate with events that are associated with that attribute or set ofattributes. The measure of correlation will vary by the level ofimportance of that attribute in a specific business. For example, if allnetwork equipment companies share the same customers, the loss of amajor customer like Cisco, a giant network company, will impact all thecompanies. The impact, however, will be greater if Cisco is thecompany's sole customer than if Cisco is less than 5% of a company'sbusiness. In this way, grouping companies in risk groups that aredefined by attributes provides a method for portfolio managers toorganize, segment, or stratify securities in groups that correlate withspecific attribute-related events. In addition, most attributes are, inturn, part of larger attribute groups. When the large telecommunicationscompany Nortel went bankrupt, all the companies that shared it as acustomer were also part of a network equipment group which in turn waspart of a communication equipment group which in turn was part of alarger digital technology group, all of which were exposed to thebankruptcy. In this way, using functional attributes enables a portfoliomanager to group securities by both broad and narrow categories and bythe importance of these categories in determining the performance ofindividual securities.

Endogenous economic models characterize functional-attributes, whichrepresent risk-related properties, qualities, or characteristics. Codingfor these attributes in a coordinate-based or ordered tagging systemenables a computer to associate tags with specific risks and generatecompany groupings that share these attributes. These risk-basedcomputer-generated groupings may be tested, as a non-limiting example,for correlations with their constituent companies, with other groups,other tags, or other individual companies or securities. In an iterativeprocess, a computer can use the tags in this way to test and validatethe statistical importance of different computer-generated groupings orindividual tags used to build computer-generated risk-controlledportfolios of securities that have unique risk characteristics derivedfrom the computer-generated groupings. Further, the computerized systemdescribed herein can be used to generate an assembly of groupings,including, as a non-limiting example, a risk-stratified portfolioconsisting of stratified groupings of statistical control groups.

The process of stratification or segmentation can include dividing apopulation into subsets (called strata or segments) within which one ormore investment securities scan be placed. Stratification andsegmentation can be used in the statistical management of the portfolio,as they are used to divide a population into parts or subsets. Thecreation of defined subsets which are assigned defined proportionsenables the creation of controls to population outcomes throughstatistical methods.

A properly stratified or segmented population can be termed a controlgroup because its constituents and the weights of the subsets aredefined and can be tested. In any heterogeneous population, there tendsto exist random variance wherein a subset of the population hasdifferent characteristics, properties, or qualities than the populationas a whole. The impact of these divergent sub-populations can bemitigated by grouping the population into sub-populations that areexpected to behave differently and then ensuring that some of eachsub-population is used in studying the population as a whole. As anexample, if one were studying the output of workers, one might find thatworkers on Monday morning were less efficient than the entire rest ofthe week. However, if one did a random sample of 20 days worked during ayear, one might randomly receive a sample set that was abnormally biasedtoward Mondays. This would not be representative of the workers as thedataset was skewed to the one period when workers were least efficient.In an effort to eliminate this bias, one might stratify the populationset across five subsets consisting of one subset for each day of theweek. Random sampling would entail assigning each subset an equal numberof worker days so that the entire sample consisted of five subsets, eachwith an equal number of example days. In this way, stratification canlimit biases in a sample set and increase the probability of arepresentative outcome.

Stratification provides controls that can: 1) ensure an unbiased sampleset that is representative of the entire population; or, 2) ensure aspecific exposure to increase the likelihood of an outcome that isdesired but not necessarily representative of the underlying population.An example of the former is in clinical trials or experiments in thesocial sciences. In those cases, the experimenter is attempting to forma representative sample set against which assumptions can be varied toinvestigate how they impact the controlled population. An example of thelatter is in risk management, where different population subsets aredesigned to be relatively uncorrelated and have highly divergentoccurrences or variations. In that case, the statistician may want toweight the sample set towards a specific subclass, such as subsets thathave relatively higher or lower volatility. In both cases,stratification enables the statistician to build sample sets withrelatively predictable outcomes based on the type of stratificationmodel being implemented. The strata generally are formed based onmembers' shared attributes or characteristics. These attributes could bebased on physically identifiable attributes such as color of hair, skinor eyes, right-handedness or left-handedness. In addition, theattributes could be based on relative quantitative metrics of apopulation, such as size, speed, or age of a population.

In the context of investment securities, the value of an investmentsecurity can be directly or indirectly related to: 1) the type ofassets, liabilities, or operations that are directly or indirectlyassociated with the security, and/or 2) the specific functionalattributes associated with the assets, liabilities, inputs, outputs,products, or operations that are directly or indirectly associated withthe security.

The aggregate expected return of a composite portfolio created using themethods described herein can be determined from the expected returns ofeach individual security and its weight in the composite portfolio. Theaggregate expected volatility of the composite portfolio can bedetermined from the expected volatility and weight of individualinvestment securities and the pairwise correlations of these individualinvestment securities with one another. Because of this, the overallvolatility can be controlled, and reduced, by stratification orsegmentation of the portfolio into groups that have relatively highintra-group correlation and relatively lower inter-group correlation.

While quantitative values associated with securities are likely toexhibit significant a stationarity, qualitative attributes are likely topersist over time, driving performance characteristics with consistencyand facilitating portfolio management and index construction at scale.The data systems described herein, which enable the syntactic andfunctional tagging of hundreds of thousands of securities and thedynamic segmentation and stratification of large sets of securities bytheir associated attributes, are instrumental in enabling this process.As a non-limiting example, by dividing the securities into correlationclusters, i.e., groupings formed based on attributes that correspond torisks, volatility can be controlled.

Syntactic Attributes

The attributes described above can be represented in a syntax whichdefines the structure of the composite units and composite portfolios.The structures can be defined by the use of syntax and architecturalpositions or coordinates, including the identification of attributesrelated to data entities that are associated with syntactic positions.Syntactic tags can have relational attributes that enable syntacticpositions to be related to each other.

As used herein, in some embodiments, a syntax may comprise a set ofrules. A syntactic position can be defined as a valid position based onthe set of rules. As a non-limiting example, a syntax may be representedin coordinate space in an arbitrary number of dimensions.

A symbol in a database corresponds to a data entity. In someembodiments, a syntactic tag associates a symbol and a rule, where thesymbols are constituents of a lexicon, and the symbols can be combinedto form valid expressions according to principles of the syntax. Asyntactic tag associates the data entity marked by a symbol to the otherdata entities based on the syntax-established set of rules. In someembodiments, the process of syntactic tagging provides a means forrelating domain-specific information. It takes information in a domainand tags it with rules that relate it in the domain. Syntactic tags canbe dynamic.

In some embodiments, a syntax can be used to evaluate the validity ofexpressions in a system. A symbol in a database can be used to mark adata entity. A syntactic tag can be used to mark the association betweena symbol and a mechanism for evaluating the validity of expressions. Thetags may be of multiple types, including syntactic attribute tags whichascribe relationships between symbols and rules that characterizeattributes. In some embodiments, a syntactic tag associates the dataentity marked by a symbol to the other data entities based on thesyntax-established set of principles. As a non-limiting example, thisprocess of syntactic tagging provides a means for relating informationwithin a domain or a subset thereof, or across domains.

Syntactic tags can have some or all of the following properties:

Expressions can be combinations of labels for tags. In some embodiments,such expressions can conform to a syntax expressible in BNF (BackusNormal Form or Backus-Naur Form) notation or an equivalentmeta-notation.

Any valid expression or sub-expression consisting of more than oneelement of the syntax, can form a locus.

Any element of the syntax that has a range of potential values describesa dimension in a discrete multidimensional space consisting of thedimensions associated with all such elements.

Any expression or sub-expression of the syntax, containing elementswhich have a range of potential values, may be stratified, in which casethat expression or sub-expression describes a dimension which consistsof regions and successive sub-regions within the multi-dimensionalspace. As a default, elements of syntax which are designated asstratified are interpreted from left to right according to theirposition within the expression, as successive levels from top to bottomwithin the architecture.

Syntax can represent coordinates that provide successive specialization;the degree of specialization grows with the depth of the architecture.The syntax can also provide step-wise serialization at each level; thedegree of serialization grows with the number of elements at each level.

In some embodiments, at each level of specialization and/or degree ofserialization, the syntax elements share a proximate syntactic positionwith both:

a) their parent in the architecture; and

b) their siblings in analogous positions across different architecturesin the same syntax.

Syntax elements may be considered to have a proximate syntactic positionif they are relatively close to other elements based on either theirspecialization or serial positions. These relationships allow forcomparison of values across syntactic positions. This property supportsapplications including but not limited to the complex structures,population sorting, autoclassification, and integration with prior arttemporal and spatial classification systems.

A functional information system (FIS) can be implemented as a databasesystem which utilizes syntactic tagging and the related concept of alocus, as a logical model for organizing data about a domain. A basicimplementation of the FIS can be achieved by having a store of thesyntactic terms of the FIS to augment the store of data entities in thedomain. Each data entity can have a reference to its location in theFIS. These table references enable searching for all data-entities in aspecific position as well as searching for the position of any dataentity in the system.

Syntactic tags are assigned to structured or unstructured data, eithermanually or via an automated process and can be associated with a uniqueidentifier for each data entity. When sets of data entities areassociated with a bounded, well-known range of objects or entities, thena lexicon containing standardized identifiers may optionally be used tofacilitate the assignment of identifiers to data entities.

As a non-limiting example, syntactic tags can be used to represent thesyntactic components of a domain-specific data entity. They can be usedfor recording and storing information that indicates to a user howspecific data entities relate to each other and/or to the specificdomain. The tags can be used to determine which data entities aresimilar and/or why they are different and or to what degree they aredifferent.

The domain-specific rules described herein can be used to characterizethe syntactic components of data entities in a domain and populate setsof domain-specific syntactic tags. They can be assigned to anydomain-specific data entity associated with a domain-specific syntacticposition. Once assigned, stored and retrievable, the data entity can nowbe related with any other data entity that shares any value on itssyntactic tag. It can be used for grouping of information based on, forexample, broad values or very specific values. If the values are broad,it provides the ability to create ever-smaller sub-sets within thecontext of the broad set. If other domains share the same syntax, thetags can be used to compare data entities in one domain to data entitiesin other domains based on shared syntax.

The rules of syntax can be based on an arbitrary number of factors. Asnon-limiting examples, they could be based on common temporal order,spatial order, anatomical, morphological, physiological, or mechanicalorder. The rules could be areas specialized to a specific domain such asthe order of its influences or of its origins. The rules could beexperimental and the validity of the rules could be tested usingsyntactic tags. In each case, the knowledge influenced by some orderingprinciple has a syntax that provides the rules for the ordering. Oncerecorded, stored, and retrievable, the process of relating data entitiesbased on syntactic tags can be based on established rules defining howdifferent data entities relate. This system can be applied to any domainand any syntax. In so doing, it provides a tool to add dimensionality toinformation from any field. It can also provide a procedure forconverting a legacy system from any field into this framework byapplying syntactic tags to the legacy codes.

Syntactic positions in the system have specific attributes that areassociated with the rules of the syntax. For example, if adomain-specific syntax is a temporally-based syntax, the attributes willbe temporally related; if it is a spatially-based syntax the attributeswill be spatially related; or, if the syntax is mechanically-based, theattributes will be mechanically related. If the syntax is sequential,the attributes will be sequentially related. If the syntax is nested,the attributes will be related to the rules of nesting.

In some embodiments, to create syntactic tags, a domain is defined, thena domain-specific syntax is defined. In one embodiment, the system canbe configured so that the specific rules of the domain-specific syntaxare fully represented in domain-specific syntactic tags.

Syntactic tagging links data entities with shared attributes byassigning each data entity to an element in the set of common syntactictags. The syntactic tags associate data entities with the other dataentities in a domain according to their syntactic associations. Thus,they inherently group and/or cluster all data entities that sharesyntactic tags.

In some embodiments, syntactic tags can be assigned to data entitieswhich have one or more attributes in common, or the same or similarmeaning, in a context of interest for the domain to which the FIS isapplied. By tagging data entities with data-entity-type tags, the systemcan operate on multiple different kinds of data within a domain or dataset. For example, data for products or markets can be added to companydata. This function can be used in connection with flagging functions,described below, to indicate that certain tags may be required only forspecific data-types.

Syntactic tags can be used to express:

(1) successive specialization, whereby all data entities that share thesame tag at a higher level also share certain common characteristics ormeanings within the domain; and the ordering of such labels within alevel is a matter of tag assignment convention, or is arbitrary; and/or

(2) a sequential process whereby all data entities that share the sametag at the next higher level also share the common characteristic thatthey are successive steps the same sequential process of the domain, atthe same level of process-detail; and the ordering of such labels withinthe category directly reflects the sequence of steps.

In some embodiments, the complete enumeration of the valid syntactictags provides a complete pre-existing model for the structures ofinterest in the domain to which the FIS model is applied, regardless ofwhether any data is actually tagged with any given label.

Syntactic tags for stratified composite units can be combined to formexpressions. Such expressions can conform to a syntax expressible in BNFnotation or an equivalent meta-notation. Any expression orsub-expression of the syntax, containing elements which have a range ofpotential values, may be stratified, in which case that expression orsub-expression describes a dimension which includes regions andsuccessive sub-regions within the multi-dimensional space.

Syntactic elements may be considered to be proximate if they arerelatively close to other elements based on either their symbolicrepresentation or serial or complementary positions. These relationshipsallow for the comparison of values across syntactic positions.

Syntactic tagging of the attributes links data entities with sharedattributes by assigning data entities to an element in the set of commonsyntactic tags. The syntactic tags associate data entities with theother data entities according to their syntactic associations. Thus,they may group or cluster data entities that share syntactic tags. Insome cases, syntactic tags can be used to create a normative model for aportfolio, discussed in more detail below.

The systems described herein can be used in combination with a barcodethat identifies a multitude of business attributes. The system canassign this standardized barcode with functional attributes andsyntactic tags to securities in a portfolio. Based on this barcode ofattributes, specific non-systematic risk exposures that exist in aportfolio can be identified. Once identified, the method can be used tocontrol for these non-systematic risks by limiting a portfolio'sexposure to these risks.

An example representation of an architecture developed from syntactictags is illustrated in FIGS. 8A and 8B. A graphical representation isillustrated in FIG. 9.

Portfolio Architecture Creation

Constructing large-scale portfolios of securities is challenging fornumerous reasons. It is difficult without both a reliable and validatedsystem of attributes as well as a stratification or segmentation systemthat uses a stratified composite architecture or segmented compositeunits to control for the large number of functional attributes thatinfluence performance at the security, group, and portfolio level.Independently and together, the systems and methods described hereinenable the engineering and management of risk exposure on a large-scalebasis.

An engineered composite of investment securities is a group ofsecurities that are engineered (or selected) to possess a differentrisk/return profile than an uncontrolled grouping from the population ofunderlying securities or the underlying risk groupings that are used toconstruct the composite.

Stratified or segmented composite portfolios comprising investmentsecurities can be based on a dynamic combination of entities of aproximate class to produce a new unit consisting of a part of each ofthe constituents being combined to create a new entity that hasdifferent properties from the underlying constituents taken separately.Dynamic properties mean that the properties of investment securitiesvary and change over time. Investment composites can be configured toaccount for this dynamic nature in order to create reliable compositesthat substantially maintain their properties over time.

A method for building a stratified composite portfolio using a syntaxfor investment securities can include the following steps: 1) groupinginvestment securities with common risk attributes; 2) stratifying orsegmenting the grouped investment securities into sub-groups that are a)associated with different risks, while b) still associated with the riskcharacteristics of the group in which they are contained.

In one embodiment, a composite portfolio can include an identificationof multiple securities and their associated weights. As a non-limitingexample, the identifications and weights can be executed using acomputerized process according to the example method illustrated inFIG. 1. As illustrated in FIG. 1, the method can first generate astratified portfolio architecture (1125) and then a resultant list ofinvestment securities and weights (1150). In an initial step, astratification module (1105) can receive as inputs investmentsecurity-related attributes (1120) and an architecture of attributerules (1122), both of which can be stored on one or more computerizeddata storage devices. As non-limiting examples, the investment securityattributes can be selected from those examples provided above. Otherattributes and types of attributes can be used.

The attribute rules can be provided by the portfolio architecture, asdescribed above. The architecture can be used to define or evaluaterelationships among attributes, tags, values, and the investmentsecurities associated with the attributes.

The stratification module (1105) can also include a selection submodule(1110) to receive, as input, a selection from a user of attributes(1120). In some embodiments, the functional attributes characterizingthe economic entities enables the construction of portfolios fromsecurities associated with those entities. As a non-limiting example, asyntax permitting the evaluation of expressions characterizing economicentities is illustrated in FIGS. 8A-8B. In other embodiments, the syntaxcan be adapted to attributes selected by the user. In other embodiments,the user can be provided with an interface for creating new structures(1121) which are then inputted to the stratification module (1105).

In some embodiments, a structure can be created from a Boolean statementin the form of ‘attribute’ ‘operator’ ‘value’ that may return true orfalse for an entity or its associated investment security based on itsattributes. In other embodiments, a structure can be created a Booleanexpression that combines (via Boolean operators) one or more statements.The lines in FIG. 9 illustrate examples.

In some embodiments, a architecture can be defined as a relationshipamong a set of structures that defines the portfolio segments, under theconstraint that any entity or its investment security that fails at onenode in the structures will not be passed through the rules of any ofthat parent's children. The stratification submodule (1115) can beconfigured to create a stratified portfolio architecture (1125) based onthe set of structures (1122), investment security attributes (1120)(optional at this stage), input regarding the creation and selection ofstructures (1121), or a listing or other identification of investmentsecurities (1131). The stratified portfolio architecture (1125) can thenbe electronically represented and stored on a computerized data storagedevice.

A structure can be derived from one or more statements that filterentities and investment securities based on attributes. As anon-limiting example, a stratified structure can be used to define arelationship among structures. Any company that is excluded from a toplevel will also be excluded from lower groups. The multiple attributesystem described herein can be configured by varying the population inany parent or child by varying one (or more) of the attributes definingthat parent or child. The ordered rules can also be expressed as a graphor network, which can be configured by enabling the population to bedynamically ordered based on functional attributes defined by thecomputerized system, the user, or a combination thereof.

Example graphical and textual representations of a resultant stratifiedportfolio architecture are illustrated in FIGS. 3 and 4. FIG. 3illustrates example attributes and their syntax. The attribute-basedrules illustrated in FIG. 3 are graphically presented in FIG. 4. Therules illustrated in FIG. 3 describe a top level composed of two groupshaving enterprise loci of real estate (1; 1205) and equipment materialsmanufacturers (2; 1210). The rules in FIG. 3 further describe enterpriseloci of real estate developers (1.A; 1215), real estate operators (1.B;1220), REITs/real estate lessors (1.C; 1225), manufacturers of materialsfor information-processing equipment (2.A; 1230), and manufacturers ofmaterials for non-information-processing equipment (2.B; 1235). Theseenterprise loci are illustrated at level two of the stratifiedarchitecture. The rules in FIG. 4 include several third-levelrelationships. The third-level defines relationships for consumer realestate developers (1.A.i; 1240), industrial real estate developers(1.A.ii; 1245) under real estate developers (1.A; 1215); North Americanreal estate operators (1.B.i; 1250), European real estate operators(1.B.i; 1255), and Asian real estate operators (1.B.i; 1260) under realestate operators (1.B; 1220); and low-leverage REITs (1.C.i; 1265) andleveraged REITs (1.C.ii; 1270) under REITs/real estate lessors (1.C;1225). Further relationships are illustrated under groups (2.A; 1230)and (2.B; 1235), but are not further described here.

Numerous attributes may be used to create a portfolio architecture. Theportfolio architecture can include a nested structure of groups. As anon-limiting example, in some instances, these groups can be formed byreferencing the attributes which are common to all entities in theuniverse, such that at each level, every element of the universe is inexactly one group. In some embodiments, these groups may be sub-dividedinto an arbitrary number of child sub-groups—and this number need not bethe same for each of the original parent groups—and this sub-divisionprocess can be carried out an arbitrary number of times, each timeadding a level to the architecture in a “top-down” manner. In someembodiments, stratified composite units are used to build largerstratified composite units, creating a structure in a “bottom-up”manner. In some embodiments, a combination of “top-down” and “bottom-up”approaches may be used. In other embodiments, existing economic andfinancial classification schemes may reconfigured using syntactictagging to make them relational and dynamic, and be partially or whollyused in the portfolio architecture in combination with any or all of theuniverse selection, weighting, reweighting, and rebalancing schemesdescribed herein. Regardless of the construction method, the resultantportfolio architecture (1125) can comprise an electronic representationof a set of attributes arranged, as non-limiting examples, in graphical,segmented, stratified, or network form, according to the definedattribute rules.

Weighting of Investment Securities

A stratified or segmented composite portfolio can be constructed of oneor more stratified or segmented composites that maintain defined riskexposures by weighting the constituents of the stratified or segmentedportfolio accordingly.

The stratification or segmentation described herein can be adjusted invarious ways to enable a user to control the population of investmentsecurities and thus the outcomes that arise from events associated witha population of investment securities. Portfolios can be adjusted, andresulting performance metrics can be engineered, based on changes madeto any or all of: 1) the population of investment securities; 2) how thepopulation of investment securities is stratified or segmented (theportfolio architecture); and, 3) how the stratification or segmentedunits are weighted within the architecture, graph, or network.

Once the portfolio architecture has been determined, weights can bedetermined for the securities. As a non-limiting example, a weightingfunction can be any function that, for a specific group in a stratifiedportfolio architecture, returns a value between −1 and 1 indicating theweight associated with that group relative to its siblings in theportfolio architecture. In some embodiments, the absolute value of aweight may exceed 1. As non-limiting examples, negative weights can beimplemented by short selling, and weights whose absolute value exceeds 1can be facilitated through leverage. In some embodiments, the sum of theweighting function for all the siblings or composites at each level orunit can be equal to 1.

In some embodiments, a security's weight is only a function of itsposition in the architecture. As a non-limiting example, among strata,weights may be divided evenly between all of the children of a givenparent group. That is, if the first level contained 10 groups, eachwould be given a weight of 10%. If one of these groups contained 4sub-groups, each would be given a weight 25% of its parent group, for aresultant weight of 25%*10%=2.5%; while if a different top-level grouphad 5 child groups, each child would weigh 20%*10%=2%. This process canbe repeated for each level, eventually yielding a weight for eachbottom-level group. A similar process can be applied to securitieswithin each bottom-level group, yielding weights for each security inthe universe.

In some embodiments, the weighting algorithm can be executed by acomputer, as follows:

class PortfolioGroup  # Returns a list of the portfolio groups  # at thesame level as this portfolio group  def siblings   ...  end  # returns aparent of this portfolio group.  # if this portfolio group does not havea  # parent, it returns undefined.  def parent   ...  end  # returns theweight that should be associated  # with this portfolio group.  defweight   num_of_siblings = self.siblings.count   if parent.is_defined?   parent_weight = self.parent.weight   else    parent_weight = 100  end   return 1/num_of_siblings * parent_weight  end end

In other embodiments, the weight of any group may be derived from theincidence attributes of the companies in that group. As a non-limitingexample, groups (formed using any of the attributes) may be weighted bya function of one or more of the attributes common to securities in theuniverse. As a non-limiting example, groups may be weighted within theirparent group proportional to the total debt of all securities in thegroup. In some embodiments, the function depends on a single attribute.In other embodiments, the function depends on a plurality of attributes.In some embodiments, the same function is used to weight every group inthe architecture. In other embodiments, different functions may be usedto weight different groups in the architecture. In some embodiments, theweighting can be executed by a computer, as follows:

class PortfolioGroup  # Returns a list of the portfolio groups  # at thesame level as this portfolio group  def siblings   ...  end  # returns aparent of this portfolio group.  # if this portfolio group does not havea  # parent, it returns undefined.  def parent   ...  end  # A functionthat for a specific group in  # a stratified portfolio architecturereturns  # a value between 0 and 1 indicating the weight  # associatedwith that group relative to its  # siblings in the portfolioarchitecture.  #  # The sum of the weighting function for the  #siblings at each level equals 1.  def weighting   ...  end  # returnsthe weight that should be associated  # with this portfolio group.  defweight( )   if parent.is_defined?    parent_weight = self.parent.weight  else    parent_weight = 100   end   return weighting * parent_weight end end

With reference to the example of FIG. 1, computerized weighting module(1130) receives the portfolio architecture (1125). As illustrated inFIG. 2, the weighting module can also be configured to receiveidentification of investment securities (1131), and identification ofattributes (1132) associated with the securities. The weighting modulecan then generate a list of securities and associated weights (1150).The weighting module is illustrated in further detail in FIG. 6. Asillustrated in FIG. 6, the system can receive a selection and/oridentification of the investment securities to be weighted (1305). Theinvestment securities to be weighted could be positioned at any point orpoints in the architecture described above. Weightings for individualsecurities and groups of securities can then be calculated for thecurrent level or segment (1310). In some embodiments, the calculationcan start at the top stratum. At the current level, the weighting schemeand rules (1315) for that level are identified. A weighting coefficientcan be calculated by dividing the outstanding proportion of weight by n,the number of investment securities or groups of securities (1320). As anon-limiting example, with reference to FIG. 4, the top-level weightingmay be calculated to be 50% to Group 1 and 50% to Group 2. At the secondlevel, Groups 1A-1C may be weighted at 0.50*.333=0.167 or 16.7% each.

Before or after calculation of the weightings, any positive or negativeweighting biases may be applied (1325). Biases can be applied byarithmetic or other operations on the weightings. In some embodiments,any biases that are applied to one group or investment security requirea corresponding opposite bias to be applied elsewhere in the same groupor in a peer group at the same level. If the bottom level has beenreached and completed, the weighting process may terminate. Otherwise,the process may continue at the next level.

The electronic representation of the weighted investment securities canthen be input as instructions to, as non-limiting examples, an exchangetraded fund (ETF) or another financial instrument such as a hedge fund,mutual fund, limited partnership or another investment vehicle.

In alternative embodiments, the steps of the method for stratification,segmentation, and weighting can be reordered. For example, the list ofinvestment securities could be introduced anywhere in the portfolioengineering process. Investment securities and/or a reconstitutionprocess could be chosen before stratification or segmentation to createexposure to a particular universe. An architecture, weighting scheme, orrebalancing scheme could be selected or chosen before or after choosingthe investment securities.

Alternative orderings and variations of the steps for creating theportfolio of investment securities described above are possible. Forexample, with reference to FIG. 1, the identification of investmentsecurities (1131) can be provided to the stratification module (1105).In that arrangement, the stratification submodule can generate thestratified portfolio architecture of investment securities (1125) thatis then input to weighting module (1130).

In some embodiments, universe identification, group selection, andperformance characteristics can be combined into one module. In otherembodiments, frames representing queries, structures, and outputs can becombined into one module. The portfolio and its constituent groups,composites, and/or securities may be represented, as non-limitingexamples, in stratified, segmented, networked, or graphical format, orin a daisy chart. In some embodiments, the outputs may be selected froma chart, geographic map, tree map, microarray, or table.

Reconstituting and Re-Weighting

Additionally, some embodiments can include reconstituting the designatedsegment or group weights on a periodic basis to maintain the desiredrisk exposures. A stratified or segmented portfolio can be comprised ofone or more stratified or segmented composite units, respectively, thatmaintain defined risk exposures by weighting the constituentsaccordingly and reconstituting the designated weights on a periodicbasis to maintain the desired risk exposures. With reference to theembodiments illustrated in FIGS. 1, 2 and 5, the steps illustrated canbe performed at any arbitrary point to create a re-weighted portfoliobased on modified inputs, such as modified weighting rules. Withreference to FIG. 5, in other embodiments, the re-weighting can beprovided by a separate re-weighting module (1155). The re-weightingmodule (1155) receives a list of target exposures assigned to portfoliogroups, composites, or constituents (1151). The re-weighting module thenselects new investment securities for inclusion in the stratifiedcomposite portfolio.

Composite Portfolio Scoring

Using methods described herein, a score can be calculated for acomposite portfolio. The score can be a characteristic of the portfolioand can be used in multiple contexts. In some embodiments, the targetscore can be a number that the portfolio is engineered to reach. Inother embodiments, the target score can be a set of attributes that aninvestor would like the portfolio to have. The portfolio score can be avalue or vector of values calculated from the portfolio which can becompared with a target score an investor has for the portfolio. Thetarget score can be a theoretical or estimated value.

A target score can be used as a way to optimize a portfolio. Theinvestor can pick the target score and the system can then be used tobuild a stratified composite portfolio optimized for that score.Alternatively, a target score can be used to build a portfolio thatreflects the performance of the underlying population. That is, thetarget score can measure expected population performance, and thestratified or segmented composite can be used to measure actualpopulation performance. Given a weighted list of securities of aportfolio and a target score, the score for the portfolio may becalculated based on derived attributes of a portfolio.

The target score can represent an estimate of expected or targetedportfolio performance. The target score can be achieved by measuring theperformance of, as non-limiting examples, individual companies, randomlysampled individual companies, stratification units, segments, and/orcomposites.

The target score can also be identified as the target score that theinvestor seeks as part of the investment objective. Here, the investormay want to use a stratified or segmented composite to reach apredetermined target score. By building groups based on commonattributes, risk groups can be formed. These risk groups may then beweighted appropriately to achieve the target score, resulting in aportfolio with known biases.

In some embodiments, a stratified or segmented composite portfolio maybe engineered to meet a user-defined target score. As non-limitingexamples, a target score could include any or all of: (a) absolutereturn goals (e.g., expected rolling rates), (b) risk/return measure(e.g., Sharpe ratio, Sortino ratio, or alpha), or (c) risk goal asmeasured by volatility (e.g., downside deviation or beta). In someembodiments, a target score may be a one- or multi-dimensional vector ofvalues or elements, such as those examples provided above. As anon-limiting example, the target score could be [the actual return—therisk free rate]/[the expected return—the risk free rate] where thetarget score is greater than or equal to one.

A method for constructing a stratified composite with a target score,according to one embodiment, is described below with reference to FIG.7. As an initial step, the user establishes a population in which toinvest by identifying a universe of investment securities (7005). Thepopulation could be, for example, financial and energy companies in theU.S. Next, the universe of securities is filtered (7015). The populationof companies is then stratified (7020). By this process, they are placedinto stratification units, or groupings based on common functional orsyntactic tags, values, or attributes.

After population stratification or segmentation, the metrics areidentified that will be used to evaluate the portfolio. The metrics usedcan depend on the population that is being stratified. For example, themetrics used for an investment-grade debt portfolio may be expectedyield and volatility, while the metrics of an equity portfolio may beexpected risk and return. Once the metrics have been identified, atarget score can be established (7010). The target score is the goalthat the user would like to see the portfolio achieve, the goal beingmeasured by the identified metrics. For example, the target score of aninvestment grade debt portfolio can be an expected yield and expectedvolatility that an investor would like the portfolio to achieve. Exampleembodiments of the target score are described below.

Once the target score is set, an engineered composite portfolio can becreated (7020). Composites can be combinations of two or morestratification units which can be engineered to reach the target score.Composites can be engineered by strategically weighting stratificationunits and the companies within the stratification units (7025) andreweighting the constituent companies (7030). The weighting andre-weighting process can include changing the population's constituents(adding or deleting constituents from the portfolio that meet thepopulation criteria).

The composite can be tested against the target score (7035). If thetarget score is accepted, the process can reach completion. If thetarget score is not satisfied, then some or all of various parameterscan be adjusted, including 1) the architecture rules, 2) the weightingrules, 3) the universe filtered through the structure and weighted, and4) the rebalancing/reconstituting policies. The process can be repeateduntil a portfolio with a satisfactory score is created.

A stratified composite can be used to optimize a portfolio. As describedabove, an engineered composite can be constructed to meet a targetscore. Here, the target score can be considered the investmentobjective. For example, the objective could be to build a compositewhose return, performance, variance, or other property, quality, orcharacteristic matches what is outlined in the target score.

Therefore, instead of building a portfolio that is most representativeof the underlying population, a portfolio can be created thatstrategically weights the lower-level groupings so that the portfoliowill match most closely its target score. Here, stratifying orsegmenting the portfolio and building composites enable theidentification of risk groups within a population. Weights thus can bestrategically allocated across them in order to meet the target score.

In investment securities, the primary concerns for investors are risk,expected return, and liquidity. Therefore, in some embodiments, thetarget score may reflect the investment objectives of the portfolioquantified with respect to the portfolio's risk, expected return, andliquidity characteristics. The goal in creating investment composites isto engineer the risk, return, and liquidity through composite design andweighting of the underlying constituents. The engineered investmentcomposites can produce composite scores (calculated from combiningindividual security data impacted by multiple attributes) that reliablycan achieve theoretical estimates.

Using the methods described herein, composites can be engineered toimprove upon these functional properties, which can be identified ordesigned for use in specific environments. In categorizing investmentsecurities, composites can be formed to manage composite scores. Astratified or segmented composite can be used to achieve a target score.Stratification or segmentation allows identified risks to be groupedwithin a portfolio. Therefore, when creating an engineered portfoliothat meets a target score, risks to which the portfolio will be exposedcan be better understood qualitatively and quantitatively.

Synthetic Conglomerates

The methods described herein may be used, as a non-limiting example, asa means to achieve through functional diversification a targeted pointon the portfolio risk-return-liquidity frontier. The data systemsdescribed herein enable the synthesis of instruments that achieve thediversification at scale sought by conglomerate managers, holdingcompanies, or investors in private equity firms, without incurring thehigh transaction costs associated with private market transactions orsignificant operating expenses. In some embodiments, a syntheticconglomerate is an engineered composite, that, as a non-limitingexample, can be configured to achieve a certain target score.

As a non-limiting example, the management of the synthetic conglomeratecan be effectuated in real-time by the data systems described herein, bypermitting the dynamic aggregation of the financial statements of eachof the constituents of large portfolios and the calculation and displayof their consolidated balance sheets, income statements, and cash flowstatements. The technologies described herein permit customizedidentification and selection of exposures within large-scale portfoliosacross functional, temporal, and geographic space.

In some embodiments, the preparation of earnings estimates and projectedfinancial statements at the portfolio, engineered composite, orsynthetic conglomerate level permit the establishment of a trackableinternal benchmark. This customized portfolio, engineered composite, orsynthetic conglomerate can be compared to those earnings estimates orfinancial statements to determine whether they met or exceededprojections. As a result, the customized portfolio, engineeredcomposite, or synthetic conglomerate can be compared reliably tointernal projections rather than relying, as other portfolios andindices are required to do, on external benchmarks.

In some embodiments, the data systems described herein enable thecreation of streams of earnings, dividends, and cash flows at theportfolio level that are more stable, consistent, and predictable thanthose at the group level. In other embodiments, the streams at the grouplevel will be more stable, consistent, and predictable than those at thesecurity level. In other embodiments, the streams at the portfolio levelwill be more stable, consistent, and predictable than those at thesecurity level. In some embodiments, the engineered composite orsynthetic conglomerate can be considered a benchmark that delivers moreconsistent, stable, and predictable returns that more reliably attainthe rates of risk and liquidity-adjusted return predicted by financialtheory than other commercially available or widely held indices orbenchmarks.

Portfolio Graph

A graph of a heterogeneous population of securities and their associatedfunctional attributes, tags, and/or values may be constructed based onan underlying functional syntax in conjunction with semantic tags andattributes, geographical and temporal data, and associated measures andmetrics. As a non-limiting example, a graph of data entitiesrepresenting a population of investment securities or financialinstruments is described below.

The investment securities or financial instruments are assigned nodes onthe graph; as non-limiting examples, the data entities may correspond tohistorical or current companies, sectors, products, securities,investments, loans, or components, aggregations, inputs, or outputsthereof.

The nodes are connected based on the relationships (demarcated by edges)between the underlying economic entities, including, but not limited to,those codified in the functional syntax, geographic or temporalrelationships, or those derived from proximate economic relationships inthe referent system, including supplier-business, intermediary-seller,work group-department, sector-industry. In some embodiments, the graphis a directed graph.

The nodes can be grouped visually based on proximity relationshipsderived from the functional syntax, the semantic tags and attributes,and associated measures and metrics. As non-limiting examples, therelationships may be ordered and represented through spectral analysis,eigenvector clustering, and k-means clustering.

In some embodiments, the edges may be weighted or colored based on theextent of interdependence among the nodes or the categoricalrelationships they reflect; as non-limiting examples, this may bederived from trade, transaction, investment, or financing data among theentities and their economic referents, commonality of semantic tags orattributes, proximity of geographic or temporal relationships, or fromthe underlying functional syntax.

In some embodiments, the nodes may be weighted or colored based on thesize or scale of the entity, or any of the categories associated withthe referent data; as non-limiting examples, this may be derived fromtrade, transaction, investment, financing, or other capital markets oraccounting-based data associated with the entity, semantic, syntactic,or functional tags or attributes associated with the entity, geographicor temporal data associated with the entity, or measures and metricsassociated with any of the foregoing.

The visual representation of the graph and its component parts may bederived from default preferences specified by the system, preferencesexpressed by one or more users, or a combination thereof.

The graph may be updated dynamically to reflect changes in therelationships among entities, permitting an visual representation of anevolutionary model for a portfolio.

Portfolio Fields

The model of the system may be represented, as a non-limiting example,as a field on which mathematical operations can be performed, whichfacilitates the study of the interactions among the economic entities orthe issuers of the investment securities.

In some embodiments, a set of economic entities E and a structure onthat set S, comprised of subsets s_(1,2 . . . n) of those economicentities, may be stored. In some embodiments, the structure is anelement of the power set of E, P(E). A set of attributesA={a_(1, 2 . . . n)} may be mapped to those subsets, based on the set aset of values V={v_(1,2 . . . n)}, such that each each a∈A is a mappinga: S→V_(a). In other embodiments, the entities may be non-economic.

In some embodiments, the field will be an ordered tuple (E, S, A). Inother embodiments, the field will be an ordered tuple (E, S, A, V). Inother embodiments, the field will not be ordered. In some embodiments,the entities can be combined to form expressions, or ordered sets whichcan be evaluated by a syntax. In other embodiments, the expressions willbe one or more combinations of entities which lack order, or whichcannot be evaluated by a syntax. In some embodiments, a portfolio,group, sub-group, stratum, or segment may be characterized as a field.

As non-limiting examples, the model of the system characterized by thefield may be syntactic, semantic, visual, qualitative, or quantitative,or some combination thereof. As non-limiting examples, the field may berepresented graphically, hierarchically, or in clustered or networkedform. In some embodiments, the representation will satisfy the formalmathematical properties of a field. In other embodiments, therepresentation will not satisfy the formal mathematical properties of afield.

Investment Returns for Securities

In some embodiments, for any given security s, its return r over a timeperiod t can be described as

k∫∫∫f _(j)(a)dwdadt+∫n _(m)(t)dt

where a_(1,2 . . . n) are the attributes in a given time period thatinfluence the return of the security, w_(1,2 . . . n) are the weights tobe assigned to each of those attributes, k is a constant, and n is a setof equations modeling stochastic components.

In some embodiments, the model can be used in conjunction with themathematical field representation to map the effects of the attributeson performance characteristics across groups of securities. In otherembodiments, this return formula can be used for predictive modeling,diagnostics, or recommendations. In some embodiments,

∫n _(m)(t)dt

will be 0; in other embodiments, it will be nonzero.

Investment Statistics for Stratified Composite Portfolios

A portfolio generated according to the methods described herein can bescored using modified versions of known statistical indicators,including, as non-limiting examples, alpha, beta, and Sharpe and Sortinoratios. A score can be generated based on a normative stratified orcomposite model portfolio and variations on the normative portfolio. Forexample, a stratified or segmented alpha can be calculated as arisk-adjusted premium to a score on normative portfolio. A stratified orsegmented beta with respect to a normalized market can also becalculated for a stratified or segmented portfolio where the normalizedmarket is defined to have a beta of 1.

In some embodiments, normative stratified or segmented betas can becalculated with respect to any market portfolio, e.g., as non-limitingexamples, stratified or segmented composite portfolios of the totalmarket, or a subset thereof. For example, the contextual subset could bedefined, as non-limiting examples, as a sector, industry, geographicregion, time period, dictionary term, or thesaurus term.

Financial Recommendation Engine

The method described herein can be used to recommend securities,composites, and portfolios to users. These recommendations are derivedfrom the securities and their referent economic entities' syntactic andempirical relationships; the functional, syntactic, semantic, temporal,geographic, financial, or economic tags, attributes, and values assignedto the economic entities; the express and revealed preferences of theusers of the database or software; and the relationships of the users inthe network.

The relationships embodied in the syntactic tags, attributes, and valuesassigned to the economic entities enable, as a non-limiting example, aninitial default calculation of proximity among them. In someembodiments, entities sharing common or proximate values or attributesassociated with a plurality of tags, loci, or partial or full sequencesthereof may be proximate within one or more databases used to providerecommendations, while entities with a plurality of disparate ordivergent values or attributes associated with a plurality of tags,loci, or partial or full sequences thereof may be disparate within thosedatabases.

Proximity may also be derived from the empirical relationships among thesecurities and economic entities, which can be aggregated, stored, andassigned to the data entities and their referents. In some embodiments,these may include [supplier-customer], [investor-entrepreneur], [impactinvestor-social enterprise], [intermediary-customer], [customer-customerof customer], [lender-borrower], [input-output], [employer-employee],[company-department], [general partner-limited partner], [serviceprovider-client], [department-work group], [subject-activity-directobject-indirect object], [parent company-subsidiary], [rawmaterial-basic component], [basic component-complex component], or[complex component-final product].

These empirical relationships may be weighted, scored, timestamped, orgeotagged, and stored in one or more databases as a basis for proximitycalculations. In some embodiments, economic entities sharing numerousrecent or heavily weighted relationships with one another, or with acommon third party, will be proximate within one or more databases usedto provide recommendations, while economic entities without commonrelationships, or whose relationships are purely historical, will bedistant within those databases.

Proximity relationships may also be derived from the non-syntacticproprietary tags, attributes, and values assigned to the economicentities and securities; as non-limiting examples, these tags may befunctional or semantic. In some embodiments, these tags, attributes, andvalues may include [raw material], [basic component], [complexcomponent], [final product], [information output], [intermediary],[department], [work group], [customer], [co-customer], [customer ofcustomer], [procurement], [transportation], [storage], [design],[production], [quality control], [sales], [exchange], [banking],[investment design], [management [audit], [capital], [energy],[information], [land], [tools], or [labor].

In addition, proximity relationships may be derived from thenon-proprietary, commonly available tags, attributes, and valuesassigned to the economic entities. As non-limiting examples, these tags,attributes, and values may include [asset class], [exchange listing],[yield], [duration], [convexity], [date founded], [location ofheadquarters], [location of incorporation], [market capitalization],[revenue], [expenses], [net income], [cash flow from operations], [cashflow from financing], or [cash flow from investing].

These tags, attributes, and values may be weighted, score, timestamped,or geotagged, and stored in one or more databases as a basis forproximity calculations. In some embodiments, economic entities orsecurities currently sharing numerous identical or similar tags,attributes, and values will be proximate within one or more databasesused to provide recommendations, while economic entities with few commontags will be distant within those databases.

The relationships, tags, attributes, and/or values, derived from one ormore databases of securities and economic entities, permit a defaultcalculation of proximity to a user. User preferences, current userholdings, and network position facilitate the customization of financialrecommendations to users based on dynamic proximity calculations.

In some embodiments, users may input their express preferences, whethersyntactic, functional, non-syntactic, non-functional, or somecombination thereof, and associated values into the system uponregistering to gain access to the database. In other embodiments, thesepreferences, or filters, may be inputted or modified at any time, eitherthrough a separate module or by indicating a preference for or againstdata entities associated with securities and economic entities. Thefilters may be absolute, in that they will permit the user to exclude orinclude certain relationships, attributes, tags, or values, or they maybe relative, in that they enable the user to indicate the extent of apreference for or against certain relationships, attributes, tags, orvalues.

As non-limiting examples, these filters may enable the user to expressabsolute or relative preferences for [market capitalization], [assetclass], [asset allocation], [funds], [expected return], [risk],[geography], [supplier], [investor], [customer], [lender-borrower],[issuer-investor], [1.1], [1.2], [1.3], [2.1], [2.2], [2.3], [3.1],[3.2], [3.3], [4.1], [4.2], [4.3], [0.1], [0.2], [0.3], [A], [B], [C],[D], [E], [F], [1i], [1ii], [1iii], [2i], [2ii], [2iii], [3i], [3ii],[3iii], [4i], [4ii], [4iii], [portfolio], [composite], [stratifiedstructure], or one or more of any of the other relationships, tags,attributes, or values assigned to the securities and economic entities.

Users may also reveal their preferences through their interactions withthe data entities on the system. In some embodiments, preferences willbe revealed by tracking user accounts, monitoring clicks, screen time,portfolio construction, and/or transactions executed, and using amachine learning process to improve dynamically the customizedrecommendations to a user based on their preferences. In someembodiments, users may upload their portfolios to the system, whoseconstituents also may be used to guide customized recommendations.

Network position may facilitate proximity calculations and dynamic,customized recommendations. The system may track connections among usersand the extent of their interactions. In some embodiments, strongconnections among users on the system will lead their recommendations toconverge significantly, weak connections will lead the recommendationsto converge slightly, and numerous degrees of separation will lead therecommendations to diverge.

In some embodiments, similarities among users in the system may lead therecommendations provided to them to converge, while differences amongthe users may lead those recommendations to diverge. As a non-limitingexample, the system may use machine learning techniques to improvedynamically the quality of customized recommendations based on changesin the network of users, their preferences, or the tags, attributes,values, or relationships assigned to the securities or economicentities.

Data Analytics

The systems syntax described herein is well-suited to organize andanalyze very large data sets associated with domains that can beeffectively studied through functional models, including, asnon-limiting examples, biology, physics, ecology, economics, computerscience, genomics, bioinformatics, aeronautics, telecommunications,electrodynamics, astronautics, finance, investment management,healthcare, medicine, epidemiology, chemistry, geology, transportation,engineering, legal systems, regulatory systems, legislative systems,political systems, and economic development. As non-limiting examples,data sets characterizing these complex systems may be hundreds ofterabytes or petabytes in size, have hundreds of thousands or millionsof elements, and have thousands of variables that significantly impactthe characteristics or features of the system. The analysis of thesecomplex systems is impracticable without an underlying functional modeland advanced customized data systems.

The assignment of tags, metatags, attributes, and values derived from anunderlying relational model of activities and resources in complexsystems facilitates the development of real-time tools to enablediagnostics, customized recommendations, and predictive analytics,thereby permitting dynamic responses to rapidly changing events. Thesereal-time tools may be particularly critical during periods in which thesystem is chaotic or far from equilibrium; as non-limiting examples,these may include perturbations, shocks, natural disasters, bubbles,panics, manias, or crashes, periods during which mechanical models ofsystems and standard database tools are likely to prove ineffective oreven harmful. As non-limiting examples, the tags, metatags, orattributes may be syntactic, semantic, morphosyntatic, morphological,physiological, anatomical, geographic, temporal, or demographic. In someembodiments, the assignment or identification of one or more tags,attributes, or values, or the proximity or similarity of tags,attributes, or values, may facilitate the assignment, identification,prediction, or recommendation of one or more other tags, attributes, orvalues. In other embodiments, the application of graph structures ornetwork models facilitates the development of these analytical tools.

Normative Cases for Stratified Portfolios

Using the systems and methods described herein, a normative stratifiedor segmented portfolio can be defined. Stratified or segmented units canbe used as a tool for building normative models and developing normativetarget scores. Reliable and validated categories of investmentsecurities can be used to sub-divide populations of securities tovalidate normative studies. The user can develop normative scores totest a hypothesis and validate a baseline for use in the comparativestudy of other stratified or segmented portfolios. The system can beconfigured so that a normative stratified portfolio can be used toderive a target score. A target score for a stratified portfolio, suchas a target alpha score, can be defined relative to a baseline normativetarget score.

A variety of statistical properties may be studied using empirical orsimulated data on a stratified or segmented portfolio, group, orsubgroup. As non-limiting examples, a statistical property may beselected from among mean, variance, standard deviation, skew, kurtosis,correlation, semivariance, and semideviation, or the excess or residualof any of these.

Statistical tests can be used to establish that for securitiesassociated with attribute-defined functional groupings of companies,commodities, securities, funds, assets, loans, or liabilities a) theyexhibit higher intra-group correlation than inter-group correlation; b)that correlation is more persistent and predictive over time thancovariance in groupings created quantitatively; c) those groupings canbe segmented or stratified in a portfolio to target or control forparticular exposures to volatility, variance, or non-systematic risk; d)the variance, standard deviation, semideviation, and/or semivariance arelower at the portfolio level than at the group level, and lower at thegroup level than at the security level; e) for a given performancemetric, exhibit more normally distributed expected or actual values thanan alternative grouping, index, or portfolio; or f) this methodologyincreases the predictability of outcomes and the extent to which returnson large portfolios consistently achieve those predicted by theory.

In some embodiments, the performance metrics may include performance,volatility, liquidity, variance, expected return, alpha, Jensen's alpha,beta, variance, covariance, semivariance, semideviation, correlation,autocorrelation, Sharpe ratio, Sortino ratio, revenue, expenses,operating expenses, earnings, net earnings, gross earnings, income,gross income, net income, cash flow, cash flow from operations, cashflow from operations, cash flow from investing, or cash flow fromfinancing. As non-limiting examples, the normality may be assessed usingthe Cramdr-von Mises criterion, the Kolmogorov-Smirnov test, theShapiro-Wilk test, the Anderson-Darling test, the Jarque-Bera test, theSiegel-Tukey test, Kuiper test, a p-value test, a Q-Q plot, a test ofskewness, or a test of kurtosis.

As non-limiting examples, this statistical methodology enables theconstruction of large and mid-cap equity portfolios that achieve aconsistent risk premium to debt over extended periods of time, aspredicted by the Capital Asset Pricing Model, and permits thedevelopment of indices that realize rates of risk and liquidity-adjustedreturn that more predictably attain the market performance posited byfinancial theory than indices such as the S&P 500™ which are frequentlyused as proxies for the market.

At an initial step, one or more theoretical or estimated scores can bedefined. Using adjustments based on changes made to at least one of thefollowing: 1) changes to the population of investment securities; 2) thestratification or segmentation methodology applied to the population ofinvestment securities; and 3) the weighting applied to the stratifiedunits or segments, the portfolio can be engineered to: 1) create arepresentative outcome for a given population (referred to herein as anormative case); 2) engineer an outcome that is statistically biased ina user-specified direction.

Depending on the adjustment methodology, the bias can be towards apopulation subset such as a geographic or temporal group or a particularfunctional attribute class (or subset of an attribute class) within aspecific population set of securities. Within a stratified or segmentedarchitecture for a given population, a specific exposure (or lackthereof) can be managed through the structure itself (either throughstructure or attribute selection) or the weighting assigned to specificunits, groups, attributes, clusters, or segments.

Non-normative composites are composites that are designed to vary fromthe normative case. Divergence from the normative case may be consideredto be an engineered or algorithmic portfolio performance metric, e.g.alpha. Using the invention, negative variance can be engineered as alphafor short investment positions. Engineering positive variance can beengineered as alpha for long investment positions. For example,distributions can be normal (based on the normative case) or non-normal.Non-normative distributions can be positively skewed (to the right ofnormal), negatively skewed (to the left of normal), platykurtic(fat-tailed), or leptokurtic (thin-tailed). Adjustments to theweightings, as described above, can be used to generate portfolioshaving these types of distributions.

In some embodiments, groupings can be used to establish pairwisecorrelation coefficients to be used in Markowitz mean-varianceoptimization. Instead of using a single correlation value for all pairsof companies, the method can be used to assign correlations to anysegment created through this methodology, by, as a non-limiting example,taking the average observed pairwise residual correlation of allsegments in a stratum, as well as a measure of out-of-groupcorrelations.

As a non-limiting example, estimating a correlation value for eachsegment in the third stratum of a stratified heterogeneous 900 securityportfolio or index and assigning the relevant value to each constituentof a group can reduce the number of correlations necessary to estimatepairwise correlations by a factor of over 200, facilitating theconstruction of an index or portfolio that will more consistently andpredictably approximate the efficient frontier than other methods ofportfolio or index construction.

Data Set Normalization and Probability Shaping

Mathematical processing according to the methods described herein can beapplied to large sets of economic and financial data to reduce thesefluctuations and randomness of the results, including, as a non-limitingexample, those of investment returns. In some embodiments, they includemultivariate algorithms can be used to organize large datasets. Themethods can be used to generate or identify causal connections andperform real-time analyses.

The system can be configured for normalizing the data sets representingsecurities. The normalization process includes statisticalcategorization based on attributes of the entity associated with thesecurity. The attributes used for normalization can be those types ofattributes described above, or other attributes relating, asnon-limiting examples, to the operations, assets, suppliers, customers,customers of customers, departments, or employees of the entityassociated with the security.

Multiple investment securities can be organized into statisticalcategories. A user interface for selecting among the attributes can beprovided by the system, which can include a statistical categorieseditor (referred to as a thesaurus editor in some embodiments). Thestatistical categories can be defined within the system using theeditor. A statistical category can be defined to be any one or more ofthe attributes described above, taken alone or in combination with oneother. The statistical categories also can be defined based on thesyntax and coding systems described above. In some cases, a statisticalcategory can also be a stratified or segmented unit.

Portfolio Powering

Matching or stratification also improves statistical power, particularlyif matching, segmentation, or stratification is based on importantprognostic variables. Such procedures, accompanied by pre-specifiedstratified or segmented analyses and sensitivity analyses may,therefore, be useful.

A prospective analysis can be used to determine a sample size requiredto achieve target statistical power. In general, the most importantcomponent affecting statistical power is sample size in the sense thatthe most frequently asked question in practice is how many observationsneed to be collected. As a non-limiting example, in assessing portfolioperformance, the null hypothesis could be that a stratified grouping hasa Sharpe ratio of 1. The alternative hypothesis could be that astratified grouping has Sharpe ratio other than 1.

Power refers to the probability that a test will find a statisticallysignificant difference when such a difference actually exists. Power isthe probability of correctly rejecting the null hypothesis. In someembodiments, power should be 0.8 or greater such that there is an 80% orgreater chance of finding a statistically significant difference whenthere is one.

Bankruptcy Example

The following example illustrates a use case for a composite ofinvestment securities. In this example, a stratified or segmentedcomposite portfolio of investment grade corporate debt securities iscreated.

Investment-grade debt is a specific class of securities with awell-defined expected rate of return and a well-defined risk. Each bondis rated by a third-party rating agency. This rating captures theestimated likelihood that the bond issuer will default on the debt. Inthe case of default risk, one of the most pertinent risks in investingin such securities, corporate bonds with the same rating should havesimilar yields to maturity, holding other variables, such as maturity,constant. The yield to maturity is the compounded annual rate of returnthat the bondholder will earn in holding the bond to maturity given itscurrent price, assuming all payments (coupon payments and face value)are made as expected. Put another way, the yield to maturity is thediscount rate that makes the present value of the bond's future cashflows, assuming all payments are made, equal to the current price of thebond. For all bonds that have a comparable rating from these agencies,the forecasted yields for a given maturity date will be the same orwithin a very tight range. That is, investment-grade corporate debtsecurities behave predictably.

While different investment-grade debt securities may have the sameestimated probability of default, the event or events that trigger adefault vary from issuer to issuer. That is, companies may facedifferent risk factors relative to the specific attribute set associatedwith the company and its operations. Some of these factors may be uniqueto that company, while others may be common to groups of companies. Suchrisks may include, as non-limiting examples, industry risk, productrisk, customer risk, sensitivity to interest rates, geographic,political, or economic factors outside a company's control, or risksrelated to the company's CEO or management in general. There are manycompany-specific attributes that can be tied to a company's defaultrisk. These can include, but are not limited to:

1) Functional operating or asset-based attributes: Such attributes arenot accounting or performance measures and indicators, but rather, asnon-limiting examples, attributes that define what a company does, suchas manufacturing or transportation; attributes or tags related to thecompany's product, such as car, computer, or couch as well as type ofcar, computer or couch; attributes related to a company's customer suchas consumer or business; attributes related to the customer's customer;attributes related to the geographic location of a business or itsindividual operations; attributes related to the products and materialsa company uses to provide its product; attributes related to any of themultivariate industries or industry segments in which a company mayoperate; attributes related to the structure of a company's businesssuch as integrated, non-integrated, forward integrated, backwardintegrated, or networked; attributes related to any of the multivariategovernmental or macroeconomic risks associated with a specific businessor country where a company does business; attributes associated with theaccounting or business risks listed by a company as core to theirbusiness; risks associated with categorization tied to a specificbusiness or segment by the investment community. At any given point intime, any one of these factors or industry events related to thesefactors may cause or increase the risk of bankruptcy in any specificcompany.

2) Management or strategy: A company has unique risks based on itsmanagement team, its decisions, and its strategies.

3) Company asset value: Bankruptcy (being one type of default)fundamentally changes the terms of the securities issued by a singlecompany. Upon filing, the presumption of returns based on ongoingoperations changes to include a liquidation scenario and the analysis ofthe rights of each security holder. In this case, investors assess theirability to receive payment on a given security based on its location inthe corporate capital structure. Securities may have been assigned apriority in liquidation. If an underlying asset of a company is sold ordisposed of, these liquidation priorities designate seniority.

4) Financial leverage: Some companies are more or less levered thanother companies.

Each attribute is a potential source of default or bankruptcy risk for afixed-income investor. Some of these attributes may relate to groups ofcompanies (e.g. companies that produce cars, or companies whoseoperations are located in New Orleans). Because of this, a portfoliothat does not control for specific attributes can be inadvertentlyexposed to a concentration in a specific risk. When a member of a groupdefaults or files for bankruptcy, other companies in that group may alsobe impacted.

The invention includes methods for building a stratified or segmentedcomposite portfolio of investment-grade corporate debt in such a waythat limits exposure to bankruptcy risks, corporate events, and othersuch non-systematic risk factors by managing the portfolio's exposure toany particular company or industry. In capitalization-weighted debtportfolios, securities are weighted in proportion to their issuance sizerelative to the total size of all issues in the portfolio. With such anunmanaged weighting scheme, it is possible for companies or industriesthat issue large amounts of debt to become over-weighted in theportfolio. If one of these companies or industries has a negative event,such as bankruptcy, then the portfolio itself will be dramaticallyimpacted. A stratified or segmented composite portfolio is a tool to capfinancial exposure to attribute-related risks.

The application of the invention to manage default risk in aninvestment-grade corporate debt portfolio provides an illustration ofone embodiment. Each debt security has a level of risk that is directlytied to the value in liquidation of the underlying assets of thecompany. This risk is distinctly separate from financial market risksassociated with the supply and demand of the debt security itself, aswell as from financial market factors that may impact the rate of returnneeded for a given investment security at a given point in time, such asthe risk free rate at that point in time.

The systems described herein protect against such non-systematic risksacross the portfolio; that is, they can reduce or eliminate materialimpacts of a single security or group of securities. This can beachieved by organizing companies in groups (strata or segments) based onnon-systematic attributes, e.g. by grouping together companies withsimilar products, or similar customer bases. In some cases,stratification or segmentation ensures that no single non-systematicexposure represents a material risk to the portfolio as a whole. In sucha composite, bankruptcy exposure is spread across enough unique groupsto minimize the impact of bankruptcies in any one group or company.

As a non-limiting example, the invention can be used to create strata orsegments as follows. For investment grade bonds, there may be severaltypes of causes of a downgrade or bankruptcy, which may include, asnon-limiting examples: 1) company-specific exposure; 2)industry-specific exposure; and 3) product-specific exposure.Investment-grade bonds of a given rating theoretically should have thesame probability ex ante of downgrade or bankruptcy risk, but thisrating provides no information about the probable causes for bankruptcy.And indeed, for bonds of the same rating, the factors that may cause theissuer to default can be radically different. These bankruptcy factors,however, are directly linked to the functional attributes of the issuingcompany. Using these attributes, it is possible to group bonds into riskgroups based on the properties of their issuers that relate to issuers'bankruptcy factors. This process may be repeated to form a nestedarchitecture of groups, where each sub-group has its own risk but alsohas risks associated with the parent group. It also may be repeated toform graphs or networks of segmented groups, where each segment andsub-segment share common risks. These risk groups, then, are the strataor segments that may be used to construct a stratified or segmentedinvestment composite, respectively. These processes reduce or mitigatethe chance that a negative event in either a single company or industrycan severely impact the portfolio.

Industry Risk Example

The following example illustrates an additional use case for stratifiedor segmented composites of investment securities. In this example, acomposite of equities from the S&P 900® index is created. This compositeis a broad-based index comprising large- and mid-cap equities issued byUS-headquartered companies from a variety of industries. This universeis a combination of the S&P 500® and S&P MidCap 400® indexes, whichtrack large- and mid-cap US companies, respectively. Over periods oftime, such a universe of equities should display a consistent returnpremium relative to a relatively risk-free investment such as USTreasury Bills.

In this example, the returns of the capitalization-weighted S&P 900® arecompared with the returns of the same universe of securities engineeredinto a stratified composite constructed using the method of theinvention. Attributes relating to the functional characteristics ofthese 900 companies are used to create nested strata that groupfunctionally similar companies together. These strata are used todetermine weights for each security following the methods describedherein. The portfolio is rebalanced quarterly, returning each securityto its initial weight.

Stratification and segmentation provide material benefits inenvironments when specific industries experience large negative priceshocks, colloquially referred to as an industry bubble “bursting”. As anindustry bubble grows, the market capitalization of the companies in theindustry grows, thus increasing that industry's weight in thecapitalization-weighted portfolio. In capitalization-weighted funds,which lack attribute-based controls on the weights of both individualcompanies and groups of similar companies, such bubbles can createunintended overexposure to specific risk groups, including those thatdisproportionately impact a particular industry. When the over-weightedindustry bubble collapses, the portfolio suffers disproportionately.Even if the companies outside of the industry bubble perform reasonably,the negative returns of the over-weighted companies can result innegative returns for the entire portfolio.

In stratified composite portfolios, however, the risk of industrybubbles can be substantially mitigated by stratifying the universe suchthat the strata correspond to distinct industry risks. In this manner,industry-specific risks are isolated and cannot inducedisproportionately negative performance in the portfolio.

The growth and collapse of information technology equities from 1997 to2000 exemplifies the benefits of stratified composite portfolios. Usingfunctional attributes, a group of companies whose business functioninvolves moving, storing, or processing information is defined.Companies in this group include Microsoft, Cisco, Intel, AOL, Qualcomm,and other such information technology companies.

The twenty largest such information technology equities in the S&P 900®grew in weight over the late 1990s such that by the year 2000, theydominated the portfolio. At yearend 1997, 1998, and 1999, these twentyequities collectively weighed 11.8%, 13.7%, and 20.4% of the S&P 900®,respectively. In 2000, when the bubble collapsed, these equities fell invalue by 42.3%, while the S&P 900® as a whole returned −6.9%. Excludingthese information companies, the rest of the S&P 900@ returned 6.8%.That is, the “market-wide” downturn in 2000 was not a systematicfailure; it was the result of uncontrolled over-exposure to a singleindustry.

In a stratified composite portfolio, such industry-specific risk can becontrolled. In the example stratified composite portfolio, the sametwenty information companies were set at a weight of 2.9% and wererebalanced to this weight quarterly. In 2000, this isolated groupperformed poorly (falling in value by 59.7%), but outside of this group,the example stratified composite portfolio had healthy returns.Excluding these twenty companies, the example stratified compositeportfolio returned 21.3%. In total, the example stratified compositeportfolio returned 17.6% in the year 2000, outperforming thecapitalization-weighted portfolio of the exact same universe by 24.5%.

The performance of the capitalization-weighted S&P 900® against theexample stratified composite portfolio of the same universe demonstrateshow stratification can prevent non-systematic industry risks fromimpacting an entire portfolio.

Application: Projected Composite Units

Some embodiments can include a computer-implemented method for use in acomputer system for storing a projected composite unit of elements, thestorage medium comprising: creating a data structure for a set of dataentities in a data structure by: generating and electronically storingin a database system a logical data model having a data structurecomprising assigning an m-dimensional set of n-dimensional vectors to aset of data entities corresponding to elements of a functional system,wherein the n-dimensional vectors are assigned based on functionalattributes of the elements, a plurality of the functional attributesrepresented as an electronic tag; wherein the functional attributesgroup data entities in the logical data model, and wherein theassignment of n-dimensional vectors re-organizes the groups based ontheir functional or non-functional attributes with respect to one ormore variables within the functional system, and wherein the dataentities represent elements of the functional system as a network ofheterogeneous components, the data entities further corresponding toelements of the functional system or attributes associated with theelements ordered by their functional roles in a process convertinginputs to outputs; receiving a user input of common exposures associatedwith multiple data entities that correspond to respective multipleelements for inclusion in a projected composite unit; projecting themultiple data entities that correspond to multiple elements into two ormore groups based on the electronic tags representing the commonexposures associated with the corresponding elements, wherein the firstgroup shares a first common exposure, and the second group shares asecond common exposure; electronically accessing the databaserepresentation of the projected groups; electronically assigning weightsto one or more of the data entities, wherein the assigned weight of adata entity is based on the relative location of the data entities inthe projected composite unit; electronically storing the assignedweights in association with data entities as the set of data entities inthe data structure; and receiving and storing an update to weights inthe data structure, wherein that update corresponds to a change in thefunctional system, the update based on a process converting inputs tooutputs as represented in the logical data model.

In some further embodiments the data structure creation furthercomprises: electronically assigning functional locations inn-dimensional space to data entities associated with the elements,wherein the n dimensions are ordered based on the sequence of the set ofinput-output processes; selecting a variable to normalize with respectto the functional system; selecting a statistical property associatedwith normality; using a statistical test to assess the relativenormality of a set of segmented groups; assigning a target weight to anm-dimensional set of n-dimensional vectors associated with data entitiesrepresenting a segmented group; wherein the target weight modifies thedivergence of the variable across n-dimensional space so as toameliorate the normality of the segmented group, as determined by thestatistical test; and periodically rebalancing the data entityrepresenting the segmented group to achieve the target weight.

In some further embodiments the data structure creation furthercomprises representing an organization of parent nodes and child nodesfor aggregating data entities and aggregating attributes of the dataentities in a functional information system, and the weights arecalculated such that the sum of weights of data entities in child nodesbelow a parent node equals the weight of the data entity of a parentnode, further comprising: selecting one of the segmented groups of dataentities which share a first common functional attribute; segmenting theselected group of data entities into two or more sub-groups, wherein thesub-groups are subsets of the segmented groups; weighting the two ormore segmented sub-groups; electronically storing the weightings inassociation with segmented groups; and wherein the data entities in afirst sub-group share a third common functional attribute and the dataentities in a second sub-group share a fourth common functionalattribute, wherein: one or more sub-groups are weighted based onfunctional attributes; and one or more sub-groups are weighted based onnon-functional attributes.

In some further embodiments the data structure creation furthercomprises constructing a digital representation of a functional systemssyntax, wherein the functional systems syntax can be applied by acomputer processor to generate expressions in the functional system,further comprising: algorithmically identifying unique composites to theelements based on functional locations and levels of the expressions;computing a set of metrics and outcomes associated with uniquecomposites; and using the unique composites to recombine, synthesize, orreengineer the elements to improve outcomes in the functional system.

Some further embodiments comprise simulating the performance under analternative weighting methodology or composition of elements; assigninga first group and a control group in the functional system; using theoutcome of the simulation to intervene in the first group; and assessingthe performance of the two groups and the functional system.

Some further embodiments comprise using the weights to create anindicator of performance; wherein the indicator normalizes the outcomescompared to a third-party data structure or weighting methodology, asdetermined by a test of statistical significance.

In some further embodiments the functional system is biological, and theprojected biological unit represents a functional component thereof,further comprising the use of the weights for diagnostic purposes ordefining biological structures or groups.

In some further embodiments the process of converting inputs to outputsis biological, and a set of processes comprises transcription,translation, protein folding, or gene editing.

In some further embodiments the functional system is economic, and theprojected composite unit represents a sector or component thereof,further comprising algorithmically computing a functional vicinity tomatch the composite unit to a second composite unit based on theproximity of the n-dimensional vectors associated with the data entitiescomprising the composite units.

In some further embodiments the process of converting inputs to outputsis associated with economic policy comprising industrial policy, laborpolicy, monetary policy, fiscal policy, central banking, trade policy,economic incentives, or taxation policy.

In some further embodiments the functional system is economic, and thedata entities comprise workers, jobs, skills, tasks, recruiters,employers, or workforces, further comprising performing a statisticaltest on the projected composite unit to determine the extent to whichthe projected composite unit is statistically descriptive, predictive,suggestive, or normative.

In some further embodiments the process converting inputs to outputsrepresents a set of labor relationships comprising career paths,workforce development, professional experience, training, and employmenthistory.

Some embodiments include a computer-implemented system for storing aprojected composite unit of elements, the system comprising: a set ofdata entities in a data structure, wherein the data structure wascreated by: generating and electronically storing in a database system alogical data model having a data structure comprising assigning anm-dimensional set of n-dimensional vectors to a set of data entitiescorresponding to elements of a functional system, wherein then-dimensional vectors are assigned based on functional attributes of theelements, a plurality of the functional attributes represented as anelectronic tag; wherein the functional attributes group data entities inthe logical data model, and wherein the assignment of n-dimensionalvectors re-organizes the groups based on their functional ornon-functional attributes with respect to one or more variables withinthe functional system, and wherein the data entities represent elementsof the functional system as a network of heterogeneous components, thedata entities further corresponding to elements of the functional systemor attributes associated with the elements ordered by their functionalroles in a process converting inputs to outputs; receiving a user inputof common exposures associated with multiple data entities thatcorrespond to respective multiple elements for inclusion in a projectedcomposite unit; projecting the multiple data entities that correspond tomultiple elements into two or more groups based on the electronic tagsrepresenting the common exposures associated with the correspondingelements, wherein the first group shares a first common exposure, andthe second group shares a second common exposure; electronicallyaccessing the database representation of the projected groups;electronically assigning weights to one or more of the data entities,wherein the assigned weight of a data entity is based on the relativelocation of the data entities in the projected composite unit;electronically storing the assigned weights in association with dataentities as the set of data entities in the data structure; andreceiving and storing an update to weights in the data structure,wherein that update corresponds to a change in the functional system,the update based on a process converting inputs to outputs asrepresented in the logical data model.

Some embodiments include a functional connectivity system comprising acomputing environment configured to perform a database operationutilizing a computerized representation of a functional system, thesystem comprising: an electronic data store comprising a set of dataentities in a database system, the data entities representing elementsof the functional system, wherein the functional system comprises agroup of related elements ordered by their functional roles inconverting inputs to outputs, or as the inputs, or as the outputs; anelectronic representation of a systems syntax, wherein the systemssyntax comprises a logical data model that can be applied by a computerprocessor to evaluate or generate expressions of elements, wherein theelements represent parts, processes, and interactions of an underlyingsystem; receiving a set of functional locations from a database, whereina function represents a conversion from inputs to outputs or a role orproperty in the conversion from inputs to outputs in the underlyingsystem and a functional location comprises a position of an entity as aninput, output, intermediate, relationship, or process associated withinputs, intermediates or outputs; algorithmically computing theproximity among a plurality of the entities or attributes representingthe entities based on their functional locations; and identifying ormatching the elements, the data entities, or the attributes representingthe data entities based on the proximity of their functional locations.

In some further embodiments, non-functional attributes can be used as aninput in the process of identifying or matching of entities, and whereinthe functional locations can be identified semantically, syntactically,graphically, symbolically, visually, or aurally; and whereinnon-functional attributes comprise an economic metric, a financialmetric, demographic data, geographic data, temporal data, orexperiential data.

In some further embodiments, the functional system comprises an economicsystem, and wherein a plurality of the data entities represententerprises, individuals, products, franchises, facilities, resources,government entities, industries, sectors, currencies, commodities,resources, infrastructure, independent contractors, or nonprofits,further comprising identifying or matching for communication,transaction, advertising, analytics, investment, taxation, incentiveprograms, policy measures, or donation.

In some further embodiments, the functional system comprises an economicsystem, and wherein a plurality of the data entities represent jobs,skills, tasks, workers, workforces, employers, educational institutions,training institutions, and research institutes, further comprisingidentifying or matching for recruiting, job search, hiring, laborpolicy, workforce development, skill development, training,apprenticeship, coaching, mentorship, management, leadershipdevelopment, news sharing, entrepreneurship, incubation, oracceleration.

In some further embodiments, the functional system comprises a financialsystem, and wherein a plurality of the data entities representinvestment securities, further comprising identifying or matching forinvestment, financing, lending, borrowing, analytics, transactions,mergers, acquisitions, divestitures, or restructuring.

In some further embodiments, identifying or matching improves aneconomic or financial metric comprising liquidity, transparency, pricediscovery, efficiency, a financial statement metric, a market metric, ora financial ratio, as compared to a third-party method, as determined bya test of statistical significance.

In some further embodiments, the functional system comprises abiological system, and wherein a plurality of the data entities comprisegenes, nucleotides, genetic sequences, molecules, expressed proteins,biological organisms, organelles, organs, organ systems, species, orpopulations, further comprising identifying or matching forbioinformatic analysis, synthetic genomics, gene editing, or drugdiscovery.

Some embodiments include a functional connectivity method comprising acomputing environment configured to perform a database operationutilizing a computerized representation of a functional system, themethod comprising: an electronic data store comprising a set of dataentities in a database system, the data entities representing elementsof the functional system, wherein the functional system comprises agroup of related elements ordered by their functional roles inconverting inputs to outputs, or as the inputs, or as the outputs; anelectronic representation of a systems syntax, wherein the systemssyntax comprises a logical data model that can be applied by a computerprocessor to evaluate or generate expressions of elements, wherein theelements represent parts, processes, and interactions of an underlyingsystem; receiving a set of functional locations from a database, whereina function represents a conversion from inputs to outputs or a role orproperty in the conversion from inputs to outputs in the underlyingsystem and a functional location comprises a position of an entity as aninput, output, intermediate, relationship, or process associated withinputs, intermediates or outputs; algorithmically computing theproximity among a plurality of the entities or attributes representingthe entities based on their functional locations; and identifying ormatching the elements, the data entities, or the attributes representingthe data entities based on the proximity of their functional locations.

System Architectures

The systems and methods described herein can be implemented in softwareor hardware or any combination thereof. The systems and methodsdescribed herein can be implemented using one or more computing deviceswhich may or may not be physically or logically separate from eachother. The methods may be performed by components arranged as eitheron-premise hardware, on-premise virtual systems, or hosted-privateinstances. Additionally, various aspects of the methods described hereinmay be combined or merged into other functions.

An example logical implementation of the system is illustrated in FIG.10. Relationships between tables (1005, 1010, 1015, 1020, 1025, and1030) are illustrated by arrows. As illustrated, table 1010 serves as alinking table between companies table (1005) and barcode table (1015).

An example computerized system for implementing the invention isillustrated in FIG. 11. A processor or computer system can be configuredto particularly perform some or all of the method described herein. Insome embodiments, the method can be partially or fully automated by oneor more computers or processors. The invention may be implemented usinga combination of any of hardware, firmware and/or software. The presentinvention (or any part(s) or function(s) thereof) may be implementedusing hardware, software, firmware, or a combination thereof and may beimplemented in one or more computer systems or other processing systems.In some embodiments, the illustrated system elements could be combinedinto a single hardware device or separated into multiple hardwaredevices. If multiple hardware devices are used, the hardware devicescould be physically located proximate to or remotely from each other.The embodiments of the methods described and illustrated are intended tobe illustrative and not to be limiting. For example, some or all of thesteps of the methods can be combined, rearranged, and/or omitted indifferent embodiments.

In one exemplary embodiment, the invention may be directed toward one ormore computer systems capable of carrying out the functionalitydescribed herein. Example computing devices may be, but are not limitedto, a personal computer (PC) system running any operating system suchas, but not limited to, Microsoft™ Windows™. However, the invention maynot be limited to these platforms. Instead, the invention may beimplemented on any appropriate computer system running any appropriateoperating system. Other components of the invention, such as, but notlimited to, a computing device, a communications device, mobile phone, atelephony device, a telephone, a personal digital assistant (PDA), apersonal computer (PC), a handheld PC, an interactive television (iTV),a digital video recorder (DVD), client workstations, thin clients, thickclients, proxy servers, network communication servers, remote accessdevices, client computers, server computers, routers, web servers, data,media, audio, video, telephony or streaming technology servers, etc.,may also be implemented using a computing device. Services may beprovided on demand using, e.g., but not limited to, an interactivetelevision (iTV), a video on demand system (VOD), and via a digitalvideo recorder (DVR), or other on demand viewing system.

The system may include one or more processors. The processor(s) may beconnected to a communication infrastructure, such as but not limited to,a communications bus, cross-over bar, or network, etc. The processes andprocessors need not be located at the same physical locations. In otherwords, processes can be executed at one or more geographically distantprocessors, over for example, a LAN or WAN connection. Computing devicesmay include a display interface that may forward graphics, text, andother data from the communication infrastructure for display on adisplay unit.

The computer system may also include, but is not limited to, a mainmemory, random access memory (RAM), and a secondary memory, etc. Thesecondary memory may include, for example, a hard disk drive and/or aremovable storage drive, such as a compact disk drive CD-ROM, etc. Theremovable storage drive may read from and/or write to a removablestorage unit. As may be appreciated, the removable storage unit mayinclude a computer usable storage medium having stored therein computersoftware and/or data. In some embodiments, a machine-accessible mediummay refer to any storage device used for storing data accessible by acomputer. Examples of a machine-accessible medium may include, e.g., butnot limited to: a magnetic hard disk; a floppy disk; an optical disk,like a compact disk read-only memory (CD-ROM) or a digital versatiledisk (DVD); a magnetic tape; and/or a memory chip, etc.

The processor may also include, or be operatively coupled to communicatewith, one or more data storage devices for storing data. Such datastorage devices can include, as non-limiting examples, magnetic disks(including internal hard disks and removable disks), magneto-opticaldisks, optical disks, read-only memory, random access memory, and/orflash storage. Storage devices suitable for tangibly embodying computerprogram instructions and data can also include all forms of non-volatilememory, including, for example, semiconductor memory devices, such asEPROM, EEPROM, and flash memory devices; magnetic disks such as internalhard disks and removable disks; magneto-optical disks; and CD-ROM andDVD-ROM disks. The processor and the memory can be supplemented by, orincorporated in, ASICs (application-specific integrated circuits).

The processing system can be in communication with a computerized datastorage system. The data storage system can include a non-relational orrelational data store, such as a MySQL™ or other relational database.Other physical and logical database types could be used. The data storemay be a database server, such as Microsoft SQL Server™, Oracle™, IBMDB2™, SQLITE™, or any other database software, relational or otherwise.The data store may store the information identifying syntactical tagsand any information required to operate on syntactical tags. In someembodiments, the processing system may use object-oriented programmingand may store data in objects. In these embodiments, the processingsystem may use an object-relational mapper (ORM) to store the dataobjects in a relational database. The systems and methods describedherein can be implemented using any number of physical data models. Inone example embodiment, an RDBMS can be used. In those embodiments,tables in the RDBMS can include columns that represent coordinates. Inthe case of economic systems, data representing companies, products,etc. can be stored in tables in the RDBMS. The tables can havepre-defined relationships between them. The tables can also haveadjuncts associated with the coordinates.

In alternative exemplary embodiments, secondary memory may include othersimilar devices for allowing computer programs or other instructions tobe loaded into computer system. Such devices may include, for example, aremovable storage unit and an interface. Examples of such may include aprogram cartridge and cartridge interface (such as, e.g., but notlimited to, those found in video game devices), a removable memory chip(such as, e.g., but not limited to, an erasable programmable read onlymemory (EPROM), or programmable read only memory (PROM) and associatedsocket, and other removable storage units and interfaces, which mayallow software and data to be transferred from the removable storageunit to computer system.

The computing device may also include an input device such as but notlimited to, a mouse or other pointing device such as a digitizer, and akeyboard or other data entry device (not shown). The computing devicemay also include output devices, such as but not limited to, a display,and a display interface. Computer may include input/output (I/O) devicessuch as but not limited to a communications interface, cable andcommunications path, etc. These devices may include, but are not limitedto, a network interface card, and modems. Communications interface mayallow software and data to be transferred between computer system andexternal devices.

In one or more embodiments, the present embodiments are practiced in theenvironment of a computer network or networks. The network can include aprivate network, or a public network (for example the Internet, asdescribed below), or a combination of both. The network includeshardware, software, or a combination of both.

From a telecommunications-oriented view, the network can be described asa set of hardware nodes interconnected by a communications facility,with one or more processes (hardware, software, or a combinationthereof) functioning at each such node. The processes caninter-communicate and exchange information with one another viacommunication pathways between them using interprocess communicationpathways. On these pathways, appropriate communications protocols areused.

An exemplary computer and/or telecommunications network environment inaccordance with the present embodiments may include node, which includemay hardware, software, or a combination of hardware and software. Thenodes may be interconnected via a communications network. Each node mayinclude one or more processes, executable by processors incorporatedinto the nodes. A single process may be run by multiple processors, ormultiple processes may be run by a single processor, for example.Additionally, each of the nodes may provide an interface point betweennetwork and the outside world, and may incorporate a collection ofsub-networks.

In an exemplary embodiment, the processes may communicate with oneanother through interprocess communication pathways supportingcommunication through any communications protocol. The pathways mayfunction in sequence or in parallel, continuously or intermittently. Thepathways can use any of the communications standards, protocols ortechnologies, described herein with respect to a communications network,in addition to standard parallel instruction sets used by manycomputers.

The nodes may include any entities capable of performing processingfunctions. Examples of such nodes that can be used with the embodimentsinclude computers (such as personal computers, workstations, servers, ormainframes), handheld wireless devices and wireline devices (such aspersonal digital assistants (PDAs), modem cell phones with processingcapability, wireless email devices including BlackBerry™ devices),document processing devices (such as scanners, printers, facsimilemachines, or multifunction document machines), or complex entities (suchas local-area networks or wide area networks) to which are connected acollection of processors, as described. For example, in the context ofthe present invention, a node itself can be a wide-area network (WAN), alocal-area network (LAN), a private network (such as a Virtual PrivateNetwork (VPN)), or collection of networks.

Communications between the nodes may be made possible by acommunications network. A node may be connected either continuously orintermittently with communications network. As an example, in thecontext of the present invention, a communications network can be adigital communications infrastructure providing adequate bandwidth andinformation security.

The communications network can include wireline communicationscapability, wireless communications capability, or a combination ofboth, at any frequencies, using any type of standard, protocol ortechnology. In addition, in the present embodiments, the communicationsnetwork can be a private network (for example, a VPN) or a publicnetwork (for example, the Internet).

A non-inclusive list of exemplary wireless protocols and technologiesused by a communications network may include BlueTooth™, general packetradio service (GPRS), cellular digital packet data (CDPD), mobilesolutions platform (MSP), multimedia messaging (MMS), wirelessapplication protocol (WAP), code division multiple access (CDMA), shortmessage service (SMS), wireless markup language (WML), handheld devicemarkup language (HDML), binary runtime environment for wireless (BREW),radio access network (RAN), and packet switched core networks (PS-CN).Also included are various generation wireless technologies. An exemplarynon-inclusive list of primarily wireline protocols and technologies usedby a communications network includes asynchronous transfer mode (ATM),enhanced interior gateway routing protocol (EIGRP), frame relay (FR),high-level data link control (HDLC), Internet control message protocol(ICMP), interior gateway routing protocol (IGRP), internetwork packetexchange (IPX), ISDN, point-to-point protocol (PPP), transmissioncontrol protocol/internet protocol (TCP/IP), routing informationprotocol (RIP) and user datagram protocol (UDP). As skilled persons willrecognize, any other known or anticipated wireless or wireline protocolsand technologies can be used.

Embodiments of the present invention may include apparatuses forperforming the operations herein. An apparatus may be speciallyconstructed for the desired purposes, or it may comprise a generalpurpose device selectively activated or reconfigured by a program storedin the device.

In one or more embodiments, the present embodiments are embodied inmachine-executable instructions. The instructions can be used to cause aprocessing device, for example a general-purpose or special-purposeprocessor, which is programmed with the instructions, to perform thesteps of the present invention. Alternatively, the steps of the presentinvention can be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components. Forexample, the present invention can be provided as a computer programproduct, as outlined above. In this environment, the embodiments caninclude a machine-readable medium having instructions stored on it. Theinstructions can be used to program any processor or processors (orother electronic devices) to perform a process or method according tothe present exemplary embodiments. In addition, the present inventioncan also be downloaded and stored on a computer program product. Here,the program can be transferred from a remote computer (e.g., a server)to a requesting computer (e.g., a client) by way of data signalsembodied in a carrier wave or other propagation medium via acommunication link (e.g., a modem or network connection) and ultimatelysuch signals may be stored on the computer systems for subsequentexecution).

The methods can be implemented in a computer program product accessiblefrom a computer-usable or computer-readable storage medium that providesprogram code for use by or in connection with a computer or anyinstruction execution system. A computer-usable or computer-readablestorage medium can be any apparatus that can contain or store theprogram for use by or in connection with the computer or instructionexecution system, apparatus, or device.

A data processing system suitable for storing and/or executing thecorresponding program code can include at least one processor coupleddirectly or indirectly to computerized data storage devices such asmemory elements. Input/output (I/O) devices (including but not limitedto keyboards, displays, pointing devices, etc.) can be coupled to thesystem. Network adapters may also be coupled to the system to enable thedata processing system to become coupled to other data processingsystems or remote printers or storage devices through interveningprivate or public networks. To provide for interaction with a user, thefeatures can be implemented on a computer with a display device, such asan LCD (liquid crystal display), or another type of monitor fordisplaying information to the user, and a keyboard and an input device,such as a mouse or trackball by which the user can provide input to thecomputer.

A computer program can be a set of instructions that can be used,directly or indirectly, in a computer. The systems and methods describedherein can be implemented using programming languages such as Flash™,JAVA™, C++, C, C#, Python, Visual Basic™, JavaScript™ PUP, XML, HTML,etc., or a combination of programming languages, including compiled orinterpreted languages, and can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. The software can include,but is not limited to, firmware, resident software, microcode, etc.Protocols such as SOAP/HTTP may be used in implementing interfacesbetween programming modules. The components and functionality describedherein may be implemented on any desktop operating system executing in avirtualized or non-virtualized environment, using any programminglanguage suitable for software development, including, but not limitedto, different versions of Microsoft Windows™, Apple™ Mac™, iOS™,Unix™/X-Windows™, Linux™ etc. The system could be implemented using aweb application framework, such as Ruby on Rails.

Suitable processors for the execution of a program of instructionsinclude, but are not limited to, general and special purposemicroprocessors, and the sole processor or one of multiple processors orcores, of any kind of computer. A processor may receive and storeinstructions and data from a computerized data storage device such as aread-only memory, a random access memory, both, or any combination ofthe data storage devices described herein. A processor may include anyprocessing circuitry or control circuitry operative to control theoperations and performance of an electronic device.

The systems, modules, and methods described herein can be implementedusing any combination of software or hardware elements. The systems,modules, and methods described herein can be implemented using one ormore virtual machines operating alone or in combination with one other.Any applicable virtualization solution can be used for encapsulating aphysical computing machine platform into a virtual machine that isexecuted under the control of virtualization software running on ahardware computing platform or host. The virtual machine can have bothvirtual system hardware and guest operating system software.

The systems and methods described herein can be implemented in acomputer system that includes a back-end component, such as a dataserver, or that includes a middleware component, such as an applicationserver or an Internet server, or that includes a front-end component,such as a client computer having a graphical user interface or anInternet browser, or any combination of them. The components of thesystem can be connected by any form or medium of digital datacommunication such as a communication network. Examples of communicationnetworks include, e.g., a LAN, a WAN, and the computers and networksthat form the Internet.

One or more embodiments of the invention may be practiced with othercomputer system configurations, including hand-held devices,microprocessor systems, microprocessor-based or programmable consumerelectronics, minicomputers, mainframe computers, etc. The invention mayalso be practiced in distributed computing environments where tasks areperformed by remote processing devices that are linked through anetwork.

The terms “computer program medium” and “computer readable medium” maybe used to generally refer to media such as but not limited to removablestorage drive, a hard disk installed in hard disk drive. These computerprogram products may provide software to computer system. The inventionmay be directed to such computer program products.

References to “one embodiment,” “an embodiment,” “example embodiment,”“various embodiments,” etc., may indicate that the embodiment(s) of theinvention so described may include a particular feature, structure, orcharacteristic, but not every embodiment necessarily includes theparticular feature, structure, or characteristic. Further, repeated useof the phrase “in one embodiment,” or “in an exemplary embodiment,” donot necessarily refer to the same embodiment, although they may.

In the description and claims, the terms “coupled” and “connected,”along with their derivatives, may be used. It should be understood thatthese terms may be not intended as synonyms for each other. Rather, inparticular embodiments, “connected” may be used to indicate that two ormore elements are in direct physical or electrical contact with eachother. “Coupled” may mean that two or more elements are in directphysical or electrical contact. However, “coupled” may also mean thattwo or more elements are not in direct contact with each other, but yetstill co-operate or interact with each other.

An algorithm may be here, and generally, considered to be aself-consistent sequence of acts or operations leading to a desiredresult. These include physical manipulations of physical quantities.Usually, though not necessarily, these quantities take the form ofelectrical or magnetic signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It has proven convenientat times, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbersor the like. It should be understood, however, that all of these andsimilar terms are to be associated with the appropriate physicalquantities and are merely convenient labels applied to these quantities.

Unless specifically stated otherwise, it may be appreciated thatthroughout the specification terms such as “processing,” “computing,”“calculating,” “determining,” or the like, refer to the action and/orprocesses of a computer or computing system, or similar electroniccomputing device, that manipulate and/or transform data represented asphysical, such as electronic, quantities within the computing system'sregisters and/or memories into other data similarly represented asphysical quantities within the computing system's memories, registers orother such information storage, transmission or display devices.

In a similar manner, the term “processor” may refer to any device orportion of a device that processes electronic data from registers and/ormemory to transform that electronic data into other electronic data thatmay be stored in registers and/or memory. A “computing platform” maycomprise one or more processors. As used herein, “software” processesmay include, for example, software and/or hardware entities that performwork over time, such as tasks, threads, and intelligent agents. Also,each process may refer to multiple processes, for carrying outinstructions in sequence or in parallel, continuously or intermittently.The terms “system” and “method” are used herein interchangeably insofaras the system may embody one or more methods and the methods may beconsidered as a system.

While one or more embodiments of the invention have been described,various alterations, additions, permutations and equivalents thereof areincluded within the scope of the invention.

In the description of embodiments, reference is made to the accompanyingdrawings that form a part hereof, which show by way of illustrationspecific embodiments of the claimed subject matter. It is to beunderstood that other embodiments may be used and that changes oralterations, such as structural changes, may be made. Such embodiments,changes or alterations are not necessarily departures from the scopewith respect to the intended claimed subject matter. While the stepsherein may be presented in a certain order, in some cases the orderingmay be changed so that certain inputs are provided at different times orin a different order without changing the function of the systems andmethods described. The disclosed procedures could also be executed indifferent orders. Additionally, various computations that are hereinneed not be performed in the order disclosed, and other embodimentsusing alternative orderings of the computations could be readilyimplemented. In addition to being reordered, the computations could alsobe decomposed into sub-computations with the same results.

What is claimed is:
 1. A computer-implemented method for use in acomputer system for storing a projected composite unit of elements, thestorage medium comprising: creating a data structure for a set of dataentities in a data structure by: generating and electronically storingin a database system a logical data model having a data structurecomprising assigning an m-dimensional set of n-dimensional vectors to aset of data entities corresponding to elements of a functional system,wherein the n-dimensional vectors are assigned based on functionalattributes of the elements, a plurality of the functional attributesrepresented as an electronic tag; wherein the functional attributesgroup data entities in the logical data model, and wherein theassignment of n-dimensional vectors re-organizes the groups based ontheir functional or non-functional attributes with respect to one ormore variables within the functional system, and wherein the dataentities represent elements of the functional system as a network ofheterogeneous components, the data entities further corresponding toelements of the functional system or attributes associated with theelements ordered by their functional roles in a process convertinginputs to outputs; receiving a user input of common exposures associatedwith multiple data entities that correspond to respective multipleelements for inclusion in a projected composite unit; projecting themultiple data entities that correspond to multiple elements into two ormore groups based on the electronic tags representing the commonexposures associated with the corresponding elements, wherein the firstgroup shares a first common exposure, and the second group shares asecond common exposure; electronically accessing the databaserepresentation of the projected groups; electronically assigning weightsto one or more of the data entities, wherein the assigned weight of adata entity is based on the relative location of the data entities inthe projected composite unit; electronically storing the assignedweights in association with data entities as the set of data entities inthe data structure; and receiving and storing an update to weights inthe data structure, wherein that update corresponds to a change in thefunctional system, the update based on a process converting inputs tooutputs as represented in the logical data model.